Rename Rng::gen to Rng::random

This anticipates `gen` being a keyword in Rust 2024. A deprecated alias
for `Rng::gen` stays in place to make the transition easier.

Fixes #1435.

benches/misc.rs

@@ -101,7 +101,7 @@ fn gen_1kb_u16_iter_repeat(b: &mut Bencher) {
     use core::iter;
     let mut rng = Pcg64Mcg::from_rng(&mut thread_rng()).unwrap();
     b.iter(|| {
-        let v: Vec<u16> = iter::repeat(()).map(|()| rng.gen()).take(512).collect();
+        let v: Vec<u16> = iter::repeat(()).map(|()| rng.random()).take(512).collect();
         v
     });
     b.bytes = 1024;
@@ -122,7 +122,7 @@ fn gen_1kb_u16_gen_array(b: &mut Bencher) {
     let mut rng = Pcg64Mcg::from_rng(&mut thread_rng()).unwrap();
     b.iter(|| {
         // max supported array length is 32!
-        let v: [[u16; 32]; 16] = rng.gen();
+        let v: [[u16; 32]; 16] = rng.random();
         v
     });
     b.bytes = 1024;
@@ -144,7 +144,7 @@ fn gen_1kb_u64_iter_repeat(b: &mut Bencher) {
     use core::iter;
     let mut rng = Pcg64Mcg::from_rng(&mut thread_rng()).unwrap();
     b.iter(|| {
-        let v: Vec<u64> = iter::repeat(()).map(|()| rng.gen()).take(128).collect();
+        let v: Vec<u64> = iter::repeat(()).map(|()| rng.random()).take(128).collect();
         v
     });
     b.bytes = 1024;
@@ -165,7 +165,7 @@ fn gen_1kb_u64_gen_array(b: &mut Bencher) {
     let mut rng = Pcg64Mcg::from_rng(&mut thread_rng()).unwrap();
     b.iter(|| {
         // max supported array length is 32!
-        let v: [[u64; 32]; 4] = rng.gen();
+        let v: [[u64; 32]; 4] = rng.random();
         v
     });
     b.bytes = 1024;

examples/monty-hall.rs

@@ -45,7 +45,7 @@ fn simulate<R: Rng>(random_door: &Uniform<u32>, rng: &mut R) -> SimulationResult
     let open = game_host_open(car, choice, rng);
 
     // Shall we switch?
-    let switch = rng.gen();
+    let switch = rng.random();
     if switch {
         choice = switch_door(choice, open);
     }

src/distributions/bernoulli.rs

@@ -136,7 +136,7 @@ impl Distribution<bool> for Bernoulli {
         if self.p_int == ALWAYS_TRUE {
             return true;
         }
-        let v: u64 = rng.gen();
+        let v: u64 = rng.random();
         v < self.p_int
     }
 }

src/distributions/float.rs

@@ -115,7 +115,7 @@ macro_rules! float_impls {
                 let precision = $fraction_bits + 1;
                 let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);
 
-                let value: $uty = rng.gen();
+                let value: $uty = rng.random();
                 let value = value >> $uty::splat(float_size - precision);
                 $ty::splat(scale) * $ty::cast_from_int(value)
             }
@@ -132,7 +132,7 @@ macro_rules! float_impls {
                 let precision = $fraction_bits + 1;
                 let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);
 
-                let value: $uty = rng.gen();
+                let value: $uty = rng.random();
                 let value = value >> $uty::splat(float_size - precision);
                 // Add 1 to shift up; will not overflow because of right-shift:
                 $ty::splat(scale) * $ty::cast_from_int(value + $uty::splat(1))
@@ -149,7 +149,7 @@ macro_rules! float_impls {
                 use core::$f_scalar::EPSILON;
                 let float_size = mem::size_of::<$f_scalar>() as $u_scalar * 8;
 
-                let value: $uty = rng.gen();
+                let value: $uty = rng.random();
                 let fraction = value >> $uty::splat(float_size - $fraction_bits);
                 fraction.into_float_with_exponent(0) - $ty::splat(1.0 - EPSILON / 2.0)
             }
@@ -192,11 +192,11 @@ mod tests {
 
                 // Standard
                 let mut zeros = StepRng::new(0, 0);
-                assert_eq!(zeros.gen::<$ty>(), $ZERO);
+                assert_eq!(zeros.random::<$ty>(), $ZERO);
                 let mut one = StepRng::new(1 << 8 | 1 << (8 + 32), 0);
-                assert_eq!(one.gen::<$ty>(), $EPSILON / two);
+                assert_eq!(one.random::<$ty>(), $EPSILON / two);
                 let mut max = StepRng::new(!0, 0);
-                assert_eq!(max.gen::<$ty>(), $ty::splat(1.0) - $EPSILON / two);
+                assert_eq!(max.random::<$ty>(), $ty::splat(1.0) - $EPSILON / two);
 
                 // OpenClosed01
                 let mut zeros = StepRng::new(0, 0);
@@ -234,11 +234,11 @@ mod tests {
 
                 // Standard
                 let mut zeros = StepRng::new(0, 0);
-                assert_eq!(zeros.gen::<$ty>(), $ZERO);
+                assert_eq!(zeros.random::<$ty>(), $ZERO);
                 let mut one = StepRng::new(1 << 11, 0);
-                assert_eq!(one.gen::<$ty>(), $EPSILON / two);
+                assert_eq!(one.random::<$ty>(), $EPSILON / two);
                 let mut max = StepRng::new(!0, 0);
-                assert_eq!(max.gen::<$ty>(), $ty::splat(1.0) - $EPSILON / two);
+                assert_eq!(max.random::<$ty>(), $ty::splat(1.0) - $EPSILON / two);
 
                 // OpenClosed01
                 let mut zeros = StepRng::new(0, 0);

src/distributions/integer.rs

@@ -81,7 +81,7 @@ macro_rules! impl_int_from_uint {
         impl Distribution<$ty> for Standard {
             #[inline]
             fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
-                rng.gen::<$uty>() as $ty
+                rng.random::<$uty>() as $ty
             }
         }
     };
@@ -99,7 +99,7 @@ macro_rules! impl_nzint {
         impl Distribution<$ty> for Standard {
             fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
                 loop {
-                    if let Some(nz) = $new(rng.gen()) {
+                    if let Some(nz) = $new(rng.random()) {
                         break nz;
                     }
                 }

src/distributions/mod.rs

@@ -11,7 +11,7 @@
 //!
 //! 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`] and of course
+//! functionality of the [`Rng`] trait, e.g. [`Rng::random`] and of course
 //! [`Rng::sample`].
 //!
 //! Abstractly, a [probability distribution] describes the probability of
@@ -31,13 +31,13 @@
 //! # The `Standard` distribution
 //!
 //! The [`Standard`] distribution is important to mention. This is the
-//! distribution used by [`Rng::gen`] and represents the "default" way to
+//! distribution used by [`Rng::random`] and represents the "default" way to
 //! produce a random value for many different types, including most primitive
 //! types, tuples, arrays, and a few derived types. See the documentation of
 //! [`Standard`] for more details.
 //!
 //! Implementing `Distribution<T>` for [`Standard`] for user types `T` makes it
-//! possible to generate type `T` with [`Rng::gen`], and by extension also
+//! possible to generate type `T` with [`Rng::random`], and by extension also
 //! with the [`random`] function.
 //!
 //! ## Random characters
@@ -181,7 +181,7 @@ use crate::Rng;
 ///
 /// impl Distribution<MyF32> for Standard {
 ///     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> MyF32 {
-///         MyF32 { x: rng.gen() }
+///         MyF32 { x: rng.random() }
 ///     }
 /// }
 /// ```

src/distributions/other.rs

@@ -189,7 +189,7 @@ where
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Mask<T, LANES> {
         // `MaskElement` must be a signed integer, so this is equivalent
         // to the scalar `i32 < 0` method
-        let var = rng.gen::<Simd<T, LANES>>();
+        let var = rng.random::<Simd<T, LANES>>();
         var.simd_lt(Simd::default())
     }
 }
@@ -208,7 +208,7 @@ macro_rules! tuple_impl {
                 let out = ($(
                     // use the $tyvar's to get the appropriate number of
                     // repeats (they're not actually needed)
-                    rng.gen::<$tyvar>()
+                    rng.random::<$tyvar>()
                 ,)*);
 
                 // Suppress the unused variable warning for empty tuple
@@ -247,7 +247,7 @@ where Standard: Distribution<T>
         let mut buff: [MaybeUninit<T>; N] = unsafe { MaybeUninit::uninit().assume_init() };
 
         for elem in &mut buff {
-            *elem = MaybeUninit::new(_rng.gen());
+            *elem = MaybeUninit::new(_rng.random());
         }
 
         unsafe { mem::transmute_copy::<_, _>(&buff) }
@@ -260,8 +260,8 @@ where Standard: Distribution<T>
     #[inline]
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Option<T> {
         // UFCS is needed here: https://github.com/rust-lang/rust/issues/24066
-        if rng.gen::<bool>() {
-            Some(rng.gen())
+        if rng.random::<bool>() {
+            Some(rng.random())
         } else {
             None
         }
@@ -273,7 +273,7 @@ where Standard: Distribution<T>
 {
     #[inline]
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Wrapping<T> {
-        Wrapping(rng.gen())
+        Wrapping(rng.random())
     }
 }
 
@@ -300,7 +300,7 @@ mod tests {
         // Test by generating a relatively large number of chars, so we also
         // take the rejection sampling path.
         let word: String = iter::repeat(())
-            .map(|()| rng.gen::<char>())
+            .map(|()| rng.random::<char>())
             .take(1000)
             .collect();
         assert!(!word.is_empty());

src/distributions/uniform.rs

@@ -515,12 +515,12 @@ macro_rules! uniform_int_impl {
             fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
                 let range = self.range as $uty as $sample_ty;
                 if range == 0 {
-                    return rng.gen();
+                    return rng.random();
                 }
 
                 let thresh = self.thresh as $uty as $sample_ty;
                 let hi = loop {
-                    let (hi, lo) = rng.gen::<$sample_ty>().wmul(range);
+                    let (hi, lo) = rng.random::<$sample_ty>().wmul(range);
                     if lo >= thresh {
                         break hi;
                     }
@@ -563,16 +563,16 @@ macro_rules! uniform_int_impl {
                 let range = high.wrapping_sub(low).wrapping_add(1) as $uty as $sample_ty;
                 if range == 0 {
                     // Range is MAX+1 (unrepresentable), so we need a special case
-                    return Ok(rng.gen());
+                    return Ok(rng.random());
                 }
 
                 // generate a sample using a sensible integer type
-                let (mut result, lo_order) = rng.gen::<$sample_ty>().wmul(range);
+                let (mut result, lo_order) = rng.random::<$sample_ty>().wmul(range);
 
                 // if the sample is biased...
                 if lo_order > range.wrapping_neg() {
                     // ...generate a new sample to reduce bias...
-                    let (new_hi_order, _) = (rng.gen::<$sample_ty>()).wmul(range as $sample_ty);
+                    let (new_hi_order, _) = (rng.random::<$sample_ty>()).wmul(range as $sample_ty);
                     // ... incrementing result on overflow
                     let is_overflow = lo_order.checked_add(new_hi_order as $sample_ty).is_none();
                     result += is_overflow as $sample_ty;
@@ -602,11 +602,11 @@ macro_rules! uniform_int_impl {
                     return Ok(rng.gen());
                 }
 
-                let (mut result, mut lo) = rng.gen::<$sample_ty>().wmul(range);
+                let (mut result, mut lo) = rng.random::<$sample_ty>().wmul(range);
 
                 // In contrast to the biased sampler, we use a loop:
                 while lo > range.wrapping_neg() {
-                    let (new_hi, new_lo) = (rng.gen::<$sample_ty>()).wmul(range);
+                    let (new_hi, new_lo) = (rng.random::<$sample_ty>()).wmul(range);
                     match lo.checked_add(new_hi) {
                         Some(x) if x < $sample_ty::MAX => {
                             // Anything less than MAX: last term is 0
@@ -732,7 +732,7 @@ macro_rules! uniform_simd_int_impl {
                 // rejection. The replacement method does however add a little
                 // overhead. Benchmarking or calculating probabilities might
                 // reveal contexts where this replacement method is slower.
-                let mut v: Simd<$unsigned, LANES> = rng.gen();
+                let mut v: Simd<$unsigned, LANES> = rng.random();
                 loop {
                     let (hi, lo) = v.wmul(range);
                     let mask = lo.simd_ge(thresh);
@@ -747,7 +747,7 @@ macro_rules! uniform_simd_int_impl {
                         return range.simd_gt(Simd::splat(0)).select(result, v);
                     }
                     // Replace only the failing lanes
-                    v = mask.select(v, rng.gen());
+                    v = mask.select(v, rng.random());
                 }
             }
         }
@@ -970,7 +970,7 @@ macro_rules! uniform_float_impl {
 
             fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
                 // Generate a value in the range [1, 2)
-                let value1_2 = (rng.gen::<$uty>() >> $uty::splat($bits_to_discard)).into_float_with_exponent(0);
+                let value1_2 = (rng.random::<$uty>() >> $uty::splat($bits_to_discard)).into_float_with_exponent(0);
 
                 // Get a value in the range [0, 1) to avoid overflow when multiplying by scale
                 let value0_1 = value1_2 - <$ty>::splat(1.0);
@@ -1006,7 +1006,7 @@ macro_rules! uniform_float_impl {
                 loop {
                     // Generate a value in the range [1, 2)
                     let value1_2 =
-                        (rng.gen::<$uty>() >> $uty::splat($bits_to_discard)).into_float_with_exponent(0);
+                        (rng.random::<$uty>() >> $uty::splat($bits_to_discard)).into_float_with_exponent(0);
 
                     // Get a value in the range [0, 1) to avoid overflow when multiplying by scale
                     let value0_1 = value1_2 - <$ty>::splat(1.0);
@@ -1079,7 +1079,7 @@ macro_rules! uniform_float_impl {
 
                 // Generate a value in the range [1, 2)
                 let value1_2 =
-                    (rng.gen::<$uty>() >> $uty::splat($bits_to_discard)).into_float_with_exponent(0);
+                    (rng.random::<$uty>() >> $uty::splat($bits_to_discard)).into_float_with_exponent(0);
 
                 // Get a value in the range [0, 1) to avoid overflow when multiplying by scale
                 let value0_1 = value1_2 - <$ty>::splat(1.0);

src/lib.rs

@@ -29,7 +29,7 @@
 //! }
 //!
 //! let mut rng = rand::thread_rng();
-//! let y: f64 = rng.gen(); // generates a float between 0 and 1
+//! let y: f64 = rng.random(); // generates a float between 0 and 1
 //!
 //! let mut nums: Vec<i32> = (1..100).collect();
 //! nums.shuffle(&mut rng);
@@ -147,7 +147,7 @@ use crate::distributions::{Distribution, Standard};
 /// let mut rng = rand::thread_rng();
 ///
 /// for x in v.iter_mut() {
-///     *x = rng.gen();
+///     *x = rng.random();
 /// }
 /// ```
 ///
@@ -158,7 +158,7 @@ use crate::distributions::{Distribution, Standard};
 #[inline]
 pub fn random<T>() -> T
 where Standard: Distribution<T> {
-    thread_rng().gen()
+    thread_rng().random()
 }
 
 #[cfg(test)]

src/prelude.rs

@@ -15,7 +15,7 @@
 //! ```
 //! use rand::prelude::*;
 //! # let mut r = StdRng::from_rng(thread_rng()).unwrap();
-//! # let _: f32 = r.gen();
+//! # let _: f32 = r.random();
 //! ```
 
 #[doc(no_inline)] pub use crate::distributions::Distribution;