Improve documentation and add test

src/distributions/float.rs

@@ -14,6 +14,32 @@ use core::mem;
 use Rng;
 use distributions::{Distribution, Uniform};
 
+/// Generate a floating point number in the half-open interval `[0, 1)` with a
+/// uniform distribution.
+///
+/// This is different from `Uniform` in that it uses all 32 bits of an RNG for a
+/// `f32`, instead of only 23, the number of bits that fit in a floats fraction
+/// (or 64 instead of 52 bits for a `f64`).
+///
+/// The smallest interval between values that can be generated is 2^-32
+/// (2.3283064e-10) for `f32`, and 2^-64 (5.421010862427522e-20) for `f64`.
+/// But this interval increases further away from zero because of limitations of
+/// the floating point format. Close to 1.0 the interval is 2^-24 (5.9604645e-8)
+/// for `f32`, and 2^-53 (1,1102230246251565) for `f64`. Compare this with
+/// `Uniform`, which has a fixed interval of 2^23 and 2^-52 respectively.
+///
+/// Note: in the future this may change to request even more bits from the RNG
+/// if the value gets very close to 0.0, so it always has as many digits of
+/// precision as the float can represent.
+///
+/// # Example
+/// ```rust
+/// use rand::{NewRng, SmallRng, Rng};
+/// use rand::distributions::HighPrecision01;
+///
+/// let val: f32 = SmallRng::new().sample(HighPrecision01);
+/// println!("f32 from [0,1): {}", val);
+/// ```
 #[derive(Clone, Copy, Debug)]
 pub struct HighPrecision01;
 
@@ -56,7 +82,7 @@ macro_rules! float_impls {
             /// use rand::distributions::Uniform;
             ///
             /// let val: f32 = SmallRng::new().sample(Uniform);
-            /// println!("f32 from (0,1): {}", val);
+            /// println!("f32 from [0,1): {}", val);
             /// ```
             fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
                 const EPSILON: $ty = 1.0 / (1u64 << $fraction_bits) as $ty;
@@ -69,10 +95,10 @@ macro_rules! float_impls {
         }
 
         impl Distribution<$ty> for HighPrecision01 {
-            /// Generate a floating point number in the open interval `(0, 1)`
-            /// (not including either endpoint) with a uniform distribution.
+            /// Generate a floating point number in the half-open interval
+            /// `[0, 1)` with a uniform distribution.
             ///
-            /// This is different from `Uniform` in that it it uses all 32 bits
+            /// This is different from `Uniform` in that it uses all 32 bits
             /// of an RNG for a `f32`, instead of only 23, the number of bits
             /// that fit in a floats fraction (or 64 instead of 52 bits for a
             /// `f64`).
@@ -83,7 +109,7 @@ macro_rules! float_impls {
             /// use rand::distributions::HighPrecision01;
             ///
             /// let val: f32 = SmallRng::new().sample(HighPrecision01);
-            /// println!("f32 from (0,1): {}", val);
+            /// println!("f32 from [0,1): {}", val);
             /// ```
             ///
             /// # Algorithm
@@ -135,6 +161,7 @@ float_impls! { f64, u64, 52, 1023, next_u64 }
 mod tests {
     use Rng;
     use mock::StepRng;
+    use super::HighPrecision01;
 
     const EPSILON32: f32 = ::core::f32::EPSILON;
     const EPSILON64: f64 = ::core::f64::EPSILON;
@@ -157,4 +184,18 @@ mod tests {
         assert_eq!(max.gen::<f32>(), 1.0 - EPSILON32 / 2.0);
         assert_eq!(max.gen::<f64>(), 1.0 - EPSILON64 / 2.0);
     }
+
+    #[test]
+    fn high_precision_01_edge_cases() {
+        // Test that the distribution is a half-open range over [0,1).
+        // These constants happen to generate the lowest and highest floats in
+        // the range.
+        let mut zeros = StepRng::new(0, 0);
+        assert_eq!(zeros.sample::<f32, _>(HighPrecision01), 0.0);
+        assert_eq!(zeros.sample::<f64, _>(HighPrecision01), 0.0);
+
+        let mut ones = StepRng::new(0xffff_ffff_ffff_ffff, 0);
+        assert_eq!(ones.sample::<f32, _>(HighPrecision01), 0.99999994);
+        assert_eq!(ones.sample::<f64, _>(HighPrecision01), 0.9999999999999999);
+    }
 }

src/lib.rs

@@ -539,7 +539,7 @@ pub trait Rng: RngCore {
         n <= 1 || self.gen_range(0, n) == 0
     }
 
-    /// Return a bool with a `p` probability of being true.
+    /// Return a bool with a probability `p` of being true.
     ///
     /// # Example
     ///