Change gen_range to accept Range types

This adds support for `Rng::range(a..b)` and `Rng::range(a..=b)`,
replacing `Rng::range(a, b)`. `a` and `b` must now implement
`PartialEq`.

This is a breaking change. In most cases, replacing
`Rng::gen_range(a, b)` with `Rng::gen_range(a..b)` should be enough,
however type annotations may be necessary, and `a` and `b` can no longer
be references or SIMD types.

SIMD types are still supported by `UniformSampler::sample_single`.

Some clippy warnings were fixed as well.

Author: vks

CHANGELOG.md

@@ -13,6 +13,10 @@ You may also find the [Upgrade Guide](https://rust-random.github.io/book/update.
 - impl PartialEq+Eq for StdRng, SmallRng, and StepRng (#975)
 - Added a `serde1` feature and added Serialize/Deserialize to `UniformInt` and `WeightedIndex` (#974)
 
+### Changes
+- `gen_range(a, b)` was replaced with `gen_range(a..b)`, and `gen_range(a..=b)`
+  is supported (#744, #1003). Note that `a` and `b` can no longer be references.
+
 ## [0.7.3] - 2020-01-10
 ### Fixes
 - The `Bernoulli` distribution constructors now reports an error on NaN and on

benches/distributions.rs

@@ -197,7 +197,7 @@ macro_rules! gen_range_int {
                 let mut high = $high;
                 let mut accum: $ty = 0;
                 for _ in 0..RAND_BENCH_N {
-                    accum = accum.wrapping_add(rng.gen_range($low, high));
+                    accum = accum.wrapping_add(rng.gen_range($low..high));
                     // force recalculation of range each time
                     high = high.wrapping_add(1) & std::$ty::MAX;
                 }
@@ -237,7 +237,7 @@ macro_rules! gen_range_float {
                 let mut low = $low;
                 let mut accum: $ty = 0.0;
                 for _ in 0..RAND_BENCH_N {
-                    accum += rng.gen_range(low, high);
+                    accum += rng.gen_range(low..high);
                     // force recalculation of range each time
                     low += 0.9;
                     high += 1.1;

src/distributions/mod.rs

@@ -11,8 +11,8 @@
 //!
 //! This module is the home of the [`Distribution`] trait and several of its
 //! implementations. It is the workhorse behind some of the convenient
-//! functionality of the [`Rng`] trait, e.g. [`Rng::gen`], [`Rng::gen_range`] and
-//! of course [`Rng::sample`].
+//! functionality of the [`Rng`] trait, e.g. [`Rng::gen`] and of course
+//! [`Rng::sample`].
 //!
 //! Abstractly, a [probability distribution] describes the probability of
 //! occurrence of each value in its sample space.
@@ -54,16 +54,16 @@
 //! space to be specified as an arbitrary range within its target type `T`.
 //! Both [`Standard`] and [`Uniform`] are in some sense uniform distributions.
 //!
-//! Values may be sampled from this distribution using [`Rng::gen_range`] or
+//! Values may be sampled from this distribution using [`Rng::sample(Range)`] or
 //! by creating a distribution object with [`Uniform::new`],
 //! [`Uniform::new_inclusive`] or `From<Range>`. When the range limits are not
 //! known at compile time it is typically faster to reuse an existing
-//! distribution object than to call [`Rng::gen_range`].
+//! `Uniform` object than to call [`Rng::sample(Range)`].
 //!
 //! User types `T` may also implement `Distribution<T>` for [`Uniform`],
 //! although this is less straightforward than for [`Standard`] (see the
-//! documentation in the [`uniform`] module. Doing so enables generation of
-//! values of type `T` with  [`Rng::gen_range`].
+//! documentation in the [`uniform`] module). Doing so enables generation of
+//! values of type `T` with  [`Rng::sample(Range)`].
 //!
 //! ## Open and half-open ranges
 //!
@@ -315,11 +315,10 @@ where
 /// multiplicative method: `(rng.gen::<$uty>() >> N) as $ty * (ε/2)`.
 ///
 /// See also: [`Open01`] which samples from `(0, 1)`, [`OpenClosed01`] which
-/// samples from `(0, 1]` and `Rng::gen_range(0, 1)` which also samples from
-/// `[0, 1)`. Note that `Open01` and `gen_range` (which uses [`Uniform`]) use
-/// transmute-based methods which yield 1 bit less precision but may perform
-/// faster on some architectures (on modern Intel CPUs all methods have
-/// approximately equal performance).
+/// samples from `(0, 1]` and `Rng::gen_range(0..1)` which also samples from
+/// `[0, 1)`. Note that `Open01` uses transmute-based methods which yield 1 bit
+/// less precision but may perform faster on some architectures (on modern Intel
+/// CPUs all methods have approximately equal performance).
 ///
 /// [`Uniform`]: uniform::Uniform
 #[derive(Clone, Copy, Debug)]

src/distributions/uniform.rs

@@ -1,4 +1,4 @@
-// Copyright 2018 Developers of the Rand project.
+// Copyright 2018-2020 Developers of the Rand project.
 // Copyright 2017 The Rust Project Developers.
 //
 // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
@@ -34,7 +34,7 @@
 //! let side = Uniform::new(-10.0, 10.0);
 //!
 //! // sample between 1 and 10 points
-//! for _ in 0..rng.gen_range(1, 11) {
+//! for _ in 0..rng.gen_range(1..=10) {
 //!     // sample a point from the square with sides -10 - 10 in two dimensions
 //!     let (x, y) = (rng.sample(side), rng.sample(side));
 //!     println!("Point: {}, {}", x, y);
@@ -105,11 +105,12 @@
 
 #[cfg(not(feature = "std"))] use core::time::Duration;
 #[cfg(feature = "std")] use std::time::Duration;
+use core::ops::{Range, RangeInclusive};
 
 use crate::distributions::float::IntoFloat;
 use crate::distributions::utils::{BoolAsSIMD, FloatAsSIMD, FloatSIMDUtils, WideningMultiply};
 use crate::distributions::Distribution;
-use crate::Rng;
+use crate::{Rng, RngCore};
 
 #[cfg(not(feature = "std"))]
 #[allow(unused_imports)] // rustc doesn't detect that this is actually used
@@ -124,7 +125,8 @@ use serde::{Serialize, Deserialize};
 ///
 /// [`Uniform::new`] and [`Uniform::new_inclusive`] construct a uniform
 /// distribution sampling from the given range; these functions may do extra
-/// work up front to make sampling of multiple values faster.
+/// work up front to make sampling of multiple values faster. If only one sample
+/// from the range is required, [`Rng::gen_range`] can be more efficient.
 ///
 /// When sampling from a constant range, many calculations can happen at
 /// compile-time and all methods should be fast; for floating-point ranges and
@@ -139,27 +141,35 @@ use serde::{Serialize, Deserialize};
 /// are of lower quality than the high bits.
 ///
 /// Implementations must sample in `[low, high)` range for
-/// `Uniform::new(low, high)`, i.e., excluding `high`. In particular care must
+/// `Uniform::new(low, high)`, i.e., excluding `high`. In particular, care must
 /// be taken to ensure that rounding never results values `< low` or `>= high`.
 ///
 /// # Example
 ///
 /// ```
 /// use rand::distributions::{Distribution, Uniform};
 ///
-/// fn main() {
-///     let between = Uniform::from(10..10000);
-///     let mut rng = rand::thread_rng();
-///     let mut sum = 0;
-///     for _ in 0..1000 {
-///         sum += between.sample(&mut rng);
-///     }
-///     println!("{}", sum);
+/// let between = Uniform::from(10..10000);
+/// let mut rng = rand::thread_rng();
+/// let mut sum = 0;
+/// for _ in 0..1000 {
+///     sum += between.sample(&mut rng);
 /// }
+/// println!("{}", sum);
+/// ```
+///
+/// For a single sample, [`Rng::gen_range`] may be prefered:
+///
+/// ```
+/// use rand::Rng;
+///
+/// let mut rng = rand::thread_rng();
+/// println!("{}", rng.gen_range(0..10));
 /// ```
 ///
 /// [`new`]: Uniform::new
 /// [`new_inclusive`]: Uniform::new_inclusive
+/// [`Rng::gen_range`]: Rng::gen_range
 #[derive(Clone, Copy, Debug)]
 #[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
 pub struct Uniform<X: SampleUniform>(X::Sampler);
@@ -287,18 +297,19 @@ pub trait UniformSampler: Sized {
     }
 }
 
-impl<X: SampleUniform> From<::core::ops::Range<X>> for Uniform<X> {
+impl<X: SampleUniform> From<Range<X>> for Uniform<X> {
     fn from(r: ::core::ops::Range<X>) -> Uniform<X> {
         Uniform::new(r.start, r.end)
     }
 }
 
-impl<X: SampleUniform> From<::core::ops::RangeInclusive<X>> for Uniform<X> {
+impl<X: SampleUniform> From<RangeInclusive<X>> for Uniform<X> {
     fn from(r: ::core::ops::RangeInclusive<X>) -> Uniform<X> {
         Uniform::new_inclusive(r.start(), r.end())
     }
 }
 
+
 /// Helper trait similar to [`Borrow`] but implemented
 /// only for SampleUniform and references to SampleUniform in
 /// order to resolve ambiguity issues.
@@ -327,6 +338,43 @@ where Borrowed: SampleUniform
     }
 }
 
+/// Range that supports generating a single sample efficiently.
+///
+/// Any type implementing this trait can be used to specify the sampled range
+/// for `Rng::gen_range`.
+pub trait SampleRange<T> {
+    /// Generate a sample from the given range.
+    fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> T;
+
+    /// Check whether the range is empty.
+    fn is_empty(&self) -> bool;
+}
+
+impl<T: SampleUniform + PartialOrd> SampleRange<T> for Range<T> {
+    #[inline]
+    fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> T {
+        T::Sampler::sample_single(self.start, self.end, rng)
+    }
+
+    #[inline]
+    fn is_empty(&self) -> bool {
+        !(self.start < self.end)
+    }
+}
+
+impl<T: SampleUniform + PartialOrd> SampleRange<T> for RangeInclusive<T> {
+    #[inline]
+    fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> T {
+        T::Sampler::sample_single_inclusive(self.start(), self.end(), rng)
+    }
+
+    #[inline]
+    fn is_empty(&self) -> bool {
+        !(self.start() <= self.end())
+    }
+}
+
+
 ////////////////////////////////////////////////////////////////////////////////
 
 // What follows are all back-ends.
@@ -425,7 +473,7 @@ macro_rules! uniform_int_impl {
                 };
 
                 UniformInt {
-                    low: low,
+                    low,
                     // These are really $unsigned values, but store as $ty:
                     range: range as $ty,
                     z: ints_to_reject as $unsigned as $ty,
@@ -570,7 +618,7 @@ macro_rules! uniform_simd_int_impl {
                 let zone = unsigned_max - ints_to_reject;
 
                 UniformInt {
-                    low: low,
+                    low,
                     // These are really $unsigned values, but store as $ty:
                     range: range.cast(),
                     z: zone.cast(),
@@ -985,7 +1033,7 @@ impl UniformSampler for UniformDuration {
                 max_nanos,
                 secs,
             } => {
-                // constant folding means this is at least as fast as `gen_range`
+                // constant folding means this is at least as fast as `Rng::sample(Range)`
                 let nano_range = Uniform::new(0, 1_000_000_000);
                 loop {
                     let s = secs.sample(rng);
@@ -1112,7 +1160,7 @@ mod tests {
                     }
 
                     for _ in 0..1000 {
-                        let v: $ty = rng.gen_range(low, high);
+                        let v = <$ty as SampleUniform>::Sampler::sample_single(low, high, &mut rng);
                         assert!($le(low, v) && $lt(v, high));
                     }
                 }
@@ -1194,7 +1242,8 @@ mod tests {
                             assert!(low_scalar <= v && v < high_scalar);
                             let v = rng.sample(my_incl_uniform).extract(lane);
                             assert!(low_scalar <= v && v <= high_scalar);
-                            let v = rng.gen_range(low, high).extract(lane);
+                            let v = <$ty as SampleUniform>::Sampler
+                                ::sample_single(low, high, &mut rng).extract(lane);
                             assert!(low_scalar <= v && v < high_scalar);
                         }
 
@@ -1205,7 +1254,9 @@ mod tests {
 
                         assert_eq!(zero_rng.sample(my_uniform).extract(lane), low_scalar);
                         assert_eq!(zero_rng.sample(my_incl_uniform).extract(lane), low_scalar);
-                        assert_eq!(zero_rng.gen_range(low, high).extract(lane), low_scalar);
+                        assert_eq!(<$ty as SampleUniform>::Sampler
+                            ::sample_single(low, high, &mut zero_rng)
+                            .extract(lane), low_scalar);
                         assert!(max_rng.sample(my_uniform).extract(lane) < high_scalar);
                         assert!(max_rng.sample(my_incl_uniform).extract(lane) <= high_scalar);
 
@@ -1218,7 +1269,9 @@ mod tests {
                                 (-1i64 << $bits_shifted) as u64,
                             );
                             assert!(
-                                lowering_max_rng.gen_range(low, high).extract(lane) < high_scalar
+                                <$ty as SampleUniform>::Sampler
+                                    ::sample_single(low, high, &mut lowering_max_rng)
+                                    .extract(lane) < high_scalar
                             );
                         }
                     }
@@ -1266,7 +1319,7 @@ mod tests {
         use std::panic::catch_unwind;
         fn range<T: SampleUniform>(low: T, high: T) {
             let mut rng = crate::test::rng(253);
-            rng.gen_range(low, high);
+            T::Sampler::sample_single(low, high, &mut rng);
         }
 
         macro_rules! t {