Implement Rng.gen_ratio() and Bernoulli::new_ratio()

benches/misc.rs

@@ -36,6 +36,30 @@ fn misc_gen_bool_var(b: &mut Bencher) {
     })
 }
 
+#[bench]
+fn misc_gen_ratio_const(b: &mut Bencher) {
+    let mut rng = StdRng::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let mut accum = true;
+        for _ in 0..::RAND_BENCH_N {
+            accum ^= rng.gen_ratio(2, 3);
+        }
+        accum
+    })
+}
+
+#[bench]
+fn misc_gen_ratio_var(b: &mut Bencher) {
+    let mut rng = StdRng::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let mut accum = true;
+        for i in 2..(::RAND_BENCH_N as u32 + 2) {
+            accum ^= rng.gen_ratio(i, i + 1);
+        }
+        accum
+    })
+}
+
 #[bench]
 fn misc_bernoulli_const(b: &mut Bencher) {
     let mut rng = StdRng::from_rng(&mut thread_rng()).unwrap();

src/distributions/bernoulli.rs

@@ -67,6 +67,28 @@ impl Bernoulli {
         };
         Bernoulli { p_int }
     }
+
+    /// Construct a new `Bernoulli` with the probability of success of
+    /// `numerator`-in-`denominator`. I.e. `new_ratio(2, 3)` will return
+    /// a `Bernoulli` with a 2-in-3 chance, or about 67%, of returning `true`.
+    ///
+    /// If `numerator == denominator` then the returned `Bernoulli` will always
+    /// return `true`. If `numerator == 0` it will always return `false`.
+    ///
+    /// # Panics
+    ///
+    /// If `denominator == 0` or `numerator > denominator`.
+    ///
+    #[inline]
+    pub fn from_ratio(numerator: u32, denominator: u32) -> Bernoulli {
+        assert!(numerator <= denominator);
+        if numerator == denominator {
+            return Bernoulli { p_int: ::core::u64::MAX }
+        }
+        const SCALE: f64 = 2.0 * (1u64 << 63) as f64;
+        let p_int = ((numerator as f64 / denominator as f64) * SCALE) as u64;
+        Bernoulli { p_int }
+    }
 }
 
 impl Distribution<bool> for Bernoulli {
@@ -103,18 +125,27 @@ mod test {
     #[test]
     fn test_average() {
         const P: f64 = 0.3;
-        let d = Bernoulli::new(P);
-        const N: u32 = 10_000_000;
+        const NUM: u32 = 3;
+        const DENOM: u32 = 10;
+        let d1 = Bernoulli::new(P);
+        let d2 = Bernoulli::from_ratio(NUM, DENOM);
+        const N: u32 = 100_000;
 
-        let mut sum: u32 = 0;
+        let mut sum1: u32 = 0;
+        let mut sum2: u32 = 0;
         let mut rng = ::test::rng(2);
         for _ in 0..N {
-            if d.sample(&mut rng) {
-                sum += 1;
+            if d1.sample(&mut rng) {
+                sum1 += 1;
+            }
+            if d2.sample(&mut rng) {
+                sum2 += 1;
             }
         }
-        let avg = (sum as f64) / (N as f64);
+        let avg1 = (sum1 as f64) / (N as f64);
+        assert!((avg1 - P).abs() < 5e-3);
 
-        assert!((avg - P).abs() < 1e-3);
+        let avg2 = (sum2 as f64) / (N as f64);
+        assert!((avg2 - (NUM as f64)/(DENOM as f64)).abs() < 5e-3);
     }
 }

src/lib.rs

@@ -387,7 +387,7 @@ pub trait Rng: RngCore {
     /// ```
     ///
     /// [`Uniform`]: distributions/uniform/struct.Uniform.html
-    fn gen_range<T: PartialOrd + SampleUniform>(&mut self, low: T, high: T) -> T {
+    fn gen_range<T: SampleUniform>(&mut self, low: T, high: T) -> T {
         T::Sampler::sample_single(low, high, self)
     }
 
@@ -509,7 +509,8 @@ pub trait Rng: RngCore {
 
     /// Return a bool with a probability `p` of being true.
     ///
-    /// This is a wrapper around [`distributions::Bernoulli`].
+    /// See also the [`Bernoulli`] distribution, which may be faster if
+    /// sampling from the same probability repeatedly.
     ///
     /// # Example
     ///
@@ -522,15 +523,44 @@ pub trait Rng: RngCore {
     ///
     /// # Panics
     ///
-    /// If `p` < 0 or `p` > 1.
+    /// If `p < 0` or `p > 1`.
     ///
-    /// [`distributions::Bernoulli`]: distributions/bernoulli/struct.Bernoulli.html
+    /// [`Bernoulli`]: distributions/bernoulli/struct.Bernoulli.html
     #[inline]
     fn gen_bool(&mut self, p: f64) -> bool {
         let d = distributions::Bernoulli::new(p);
         self.sample(d)
     }
 
+    /// Return a bool with a probability of `numerator/denominator` of being
+    /// true. I.e. `gen_ratio(2, 3)` has chance of 2 in 3, or about 67%, of
+    /// returning true. If `numerator == denominator`, then the returned value
+    /// is guaranteed to be `true`. If `numerator == 0`, then the returned
+    /// value is guaranteed to be `false`.
+    ///
+    /// See also the [`Bernoulli`] distribution, which may be faster if
+    /// sampling from the same `numerator` and `denominator` repeatedly.
+    ///
+    /// # Panics
+    ///
+    /// If `denominator == 0` or `numerator > denominator`.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// println!("{}", rng.gen_ratio(2, 3));
+    /// ```
+    ///
+    /// [`Bernoulli`]: distributions/bernoulli/struct.Bernoulli.html
+    #[inline]
+    fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool {
+        let d = distributions::Bernoulli::from_ratio(numerator, denominator);
+        self.sample(d)
+    }
+
     /// Return a random element from `values`.
     ///
     /// Return `None` if `values` is empty.
@@ -1017,4 +1047,21 @@ mod test {
                      (u8, i8, u16, i16, u32, i32, u64, i64),
                      (f32, (f64, (f64,)))) = random();
     }
+
+    #[test]
+    fn test_gen_ratio_average() {
+        const NUM: u32 = 3;
+        const DENOM: u32 = 10;
+        const N: u32 = 100_000;
+
+        let mut sum: u32 = 0;
+        let mut rng = rng(111);
+        for _ in 0..N {
+            if rng.gen_ratio(NUM, DENOM) {
+                sum += 1;
+            }
+        }
+        let avg = (sum as f64) / (N as f64);
+        assert!((avg - (NUM as f64)/(DENOM as f64)).abs() < 1e-3);
+    }
 }