vendor/rand-0.6.1/src/distributions/bernoulli.rs

   1 // Copyright 2018 Developers of the Rand project.
   2 //
   3 // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
   4 // https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
   5 // <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
   6 // option. This file may not be copied, modified, or distributed
   7 // except according to those terms.
   8
   9 //! The Bernoulli distribution.
  10
  11 use Rng;
  12 use distributions::Distribution;
  13
  14 /// The Bernoulli distribution.
  15 ///
  16 /// This is a special case of the Binomial distribution where `n = 1`.
  17 ///
  18 /// # Example
  19 ///
  20 /// ```rust
  21 /// use rand::distributions::{Bernoulli, Distribution};
  22 ///
  23 /// let d = Bernoulli::new(0.3);
  24 /// let v = d.sample(&mut rand::thread_rng());
  25 /// println!("{} is from a Bernoulli distribution", v);
  26 /// ```
  27 ///
  28 /// # Precision
  29 ///
  30 /// This `Bernoulli` distribution uses 64 bits from the RNG (a `u64`),
  31 /// so only probabilities that are multiples of 2<sup>-64</sup> can be
  32 /// represented.
  33 #[derive(Clone, Copy, Debug)]
  34 pub struct Bernoulli {
  35     /// Probability of success, relative to the maximal integer.
  36     p_int: u64,
  37 }
  38
  39 // To sample from the Bernoulli distribution we use a method that compares a
  40 // random `u64` value `v < (p * 2^64)`.
  41 //
  42 // If `p == 1.0`, the integer `v` to compare against can not represented as a
  43 // `u64`. We manually set it to `u64::MAX` instead (2^64 - 1 instead of 2^64).
  44 // Note that  value of `p < 1.0` can never result in `u64::MAX`, because an
  45 // `f64` only has 53 bits of precision, and the next largest value of `p` will
  46 // result in `2^64 - 2048`.
  47 //
  48 // Also there is a 100% theoretical concern: if someone consistenly wants to
  49 // generate `true` using the Bernoulli distribution (i.e. by using a probability
  50 // of `1.0`), just using `u64::MAX` is not enough. On average it would return
  51 // false once every 2^64 iterations. Some people apparently care about this
  52 // case.
  53 //
  54 // That is why we special-case `u64::MAX` to always return `true`, without using
  55 // the RNG, and pay the performance price for all uses that *are* reasonable.
  56 // Luckily, if `new()` and `sample` are close, the compiler can optimize out the
  57 // extra check.
  58 const ALWAYS_TRUE: u64 = ::core::u64::MAX;
  59
  60 // This is just `2.0.powi(64)`, but written this way because it is not available
  61 // in `no_std` mode.
  62 const SCALE: f64 = 2.0 * (1u64 << 63) as f64;
  63
  64 impl Bernoulli {
  65     /// Construct a new `Bernoulli` with the given probability of success `p`.
  66     ///
  67     /// # Panics
  68     ///
  69     /// If `p < 0` or `p > 1`.
  70     ///
  71     /// # Precision
  72     ///
  73     /// For `p = 1.0`, the resulting distribution will always generate true.
  74     /// For `p = 0.0`, the resulting distribution will always generate false.
  75     ///
  76     /// This method is accurate for any input `p` in the range `[0, 1]` which is
  77     /// a multiple of 2<sup>-64</sup>. (Note that not all multiples of
  78     /// 2<sup>-64</sup> in `[0, 1]` can be represented as a `f64`.)
  79     #[inline]
  80     pub fn new(p: f64) -> Bernoulli {
  81         if p < 0.0 || p >= 1.0 {
  82             if p == 1.0 { return Bernoulli { p_int: ALWAYS_TRUE } }
  83             panic!("Bernoulli::new not called with 0.0 <= p <= 1.0");
  84         }
  85         Bernoulli { p_int: (p * SCALE) as u64 }
  86     }
  87
  88     /// Construct a new `Bernoulli` with the probability of success of
  89     /// `numerator`-in-`denominator`. I.e. `new_ratio(2, 3)` will return
  90     /// a `Bernoulli` with a 2-in-3 chance, or about 67%, of returning `true`.
  91     ///
  92     /// If `numerator == denominator` then the returned `Bernoulli` will always
  93     /// return `true`. If `numerator == 0` it will always return `false`.
  94     ///
  95     /// # Panics
  96     ///
  97     /// If `denominator == 0` or `numerator > denominator`.
  98     ///
  99     #[inline]
 100     pub fn from_ratio(numerator: u32, denominator: u32) -> Bernoulli {
 101         assert!(numerator <= denominator);
 102         if numerator == denominator {
 103             return Bernoulli { p_int: ::core::u64::MAX }
 104         }
 105         let p_int = ((numerator as f64 / denominator as f64) * SCALE) as u64;
 106         Bernoulli { p_int }
 107     }
 108 }
 109
 110 impl Distribution<bool> for Bernoulli {
 111     #[inline]
 112     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool {
 113         // Make sure to always return true for p = 1.0.
 114         if self.p_int == ALWAYS_TRUE { return true; }
 115         let v: u64 = rng.gen();
 116         v < self.p_int
 117     }
 118 }
 119
 120 #[cfg(test)]
 121 mod test {
 122     use Rng;
 123     use distributions::Distribution;
 124     use super::Bernoulli;
 125
 126     #[test]
 127     fn test_trivial() {
 128         let mut r = ::test::rng(1);
 129         let always_false = Bernoulli::new(0.0);
 130         let always_true = Bernoulli::new(1.0);
 131         for _ in 0..5 {
 132             assert_eq!(r.sample::<bool, _>(&always_false), false);
 133             assert_eq!(r.sample::<bool, _>(&always_true), true);
 134             assert_eq!(Distribution::<bool>::sample(&always_false, &mut r), false);
 135             assert_eq!(Distribution::<bool>::sample(&always_true, &mut r), true);
 136         }
 137     }
 138
 139     #[test]
 140     fn test_average() {
 141         const P: f64 = 0.3;
 142         const NUM: u32 = 3;
 143         const DENOM: u32 = 10;
 144         let d1 = Bernoulli::new(P);
 145         let d2 = Bernoulli::from_ratio(NUM, DENOM);
 146         const N: u32 = 100_000;
 147
 148         let mut sum1: u32 = 0;
 149         let mut sum2: u32 = 0;
 150         let mut rng = ::test::rng(2);
 151         for _ in 0..N {
 152             if d1.sample(&mut rng) {
 153                 sum1 += 1;
 154             }
 155             if d2.sample(&mut rng) {
 156                 sum2 += 1;
 157             }
 158         }
 159         let avg1 = (sum1 as f64) / (N as f64);
 160         assert!((avg1 - P).abs() < 5e-3);
 161
 162         let avg2 = (sum2 as f64) / (N as f64);
 163         assert!((avg2 - (NUM as f64)/(DENOM as f64)).abs() < 5e-3);
 164     }
 165 }