Rename Standard → StandardUniform (#1526)

Also remove impl for Option<T>

CHANGELOG.md

@@ -30,6 +30,8 @@ You may also find the [Upgrade Guide](https://rust-random.github.io/book/update.
 - Rename `rand::thread_rng()` to `rand::rng()`, and remove from the prelude (#1506)
 - Remove `rand::random()` from the prelude (#1506)
 - Rename `Rng::gen_range` to `random_range`, `gen_bool` to `random_bool`, `gen_ratio` to `random_ratio` (#1505)
+- Rename `Standard` to `StandardUniform` (#1526)
+- Remove impl of `Distribution<Option<T>>` for `Standard` (#1526)
 
 ## [0.9.0-alpha.1] - 2024-03-18
 - Add the `Slice::num_choices` method to the Slice distribution (#1402)

README.md

@@ -16,7 +16,7 @@ Rand is a set of crates supporting (pseudo-)random generators:
 
 With broad support for random value generation and random processes:
 
--   [`Standard`](https://docs.rs/rand/latest/rand/distributions/struct.Standard.html) random value sampling,
+-   [`StandardUniform`](https://docs.rs/rand/latest/rand/distributions/struct.StandardUniform.html) random value sampling,
     [`Uniform`](https://docs.rs/rand/latest/rand/distributions/struct.Uniform.html)-ranged value sampling
     and [more](https://docs.rs/rand/latest/rand/distr/index.html)
 -   Samplers for a large number of non-uniform random number distributions via our own

benches/benches/array.rs

@@ -9,7 +9,7 @@
 //! Generating/filling arrays and iterators of output
 
 use criterion::{criterion_group, criterion_main, Criterion};
-use rand::distr::Standard;
+use rand::distr::StandardUniform;
 use rand::prelude::*;
 use rand_pcg::Pcg64Mcg;
 
@@ -36,7 +36,7 @@ pub fn bench(c: &mut Criterion) {
     g.bench_function("u16_sample_iter", |b| {
         let mut rng = Pcg64Mcg::from_rng(&mut rand::rng());
         b.iter(|| {
-            let v: Vec<u16> = Standard.sample_iter(&mut rng).take(512).collect();
+            let v: Vec<u16> = StandardUniform.sample_iter(&mut rng).take(512).collect();
             v
         });
     });
@@ -70,7 +70,7 @@ pub fn bench(c: &mut Criterion) {
     g.bench_function("u64_sample_iter", |b| {
         let mut rng = Pcg64Mcg::from_rng(&mut rand::rng());
         b.iter(|| {
-            let v: Vec<u64> = Standard.sample_iter(&mut rng).take(128).collect();
+            let v: Vec<u64> = StandardUniform.sample_iter(&mut rng).take(128).collect();
             v
         });
     });

benches/benches/bool.rs

@@ -28,7 +28,7 @@ pub fn bench(c: &mut Criterion) {
 
     g.bench_function("standard", |b| {
         let mut rng = Pcg32::from_rng(&mut rand::rng());
-        b.iter(|| rng.sample::<bool, _>(rand::distr::Standard))
+        b.iter(|| rng.sample::<bool, _>(rand::distr::StandardUniform))
     });
 
     g.bench_function("const", |b| {

benches/benches/generators.rs

@@ -171,7 +171,7 @@ pub fn init_from_seed(c: &mut Criterion) {
 
     fn bench<R: SeedableRng>(g: &mut BenchmarkGroup<WallTime>, name: &str)
     where
-        rand::distr::Standard: Distribution<<R as SeedableRng>::Seed>,
+        rand::distr::StandardUniform: Distribution<<R as SeedableRng>::Seed>,
     {
         g.bench_function(name, |b| {
             let mut rng = Pcg32::from_os_rng();

benches/benches/standard.rs

@@ -9,7 +9,7 @@
 use core::time::Duration;
 use criterion::measurement::WallTime;
 use criterion::{criterion_group, criterion_main, BenchmarkGroup, Criterion};
-use rand::distr::{Alphanumeric, Standard};
+use rand::distr::{Alphanumeric, StandardUniform};
 use rand::prelude::*;
 use rand_distr::{Open01, OpenClosed01};
 use rand_pcg::Pcg64Mcg;
@@ -34,14 +34,14 @@ where
 }
 
 pub fn bench(c: &mut Criterion) {
-    let mut g = c.benchmark_group("Standard");
+    let mut g = c.benchmark_group("StandardUniform");
     g.sample_size(1000);
     g.warm_up_time(Duration::from_millis(500));
     g.measurement_time(Duration::from_millis(1000));
 
     macro_rules! do_ty {
         ($t:ty) => {
-            bench_ty::<$t, Standard>(&mut g, stringify!($t));
+            bench_ty::<$t, StandardUniform>(&mut g, stringify!($t));
         };
         ($t:ty, $($tt:ty),*) => {
             do_ty!($t);

rand_distr/src/cauchy.rs

@@ -9,7 +9,7 @@
 
 //! The Cauchy distribution `Cauchy(x₀, γ)`.
 
-use crate::{Distribution, Standard};
+use crate::{Distribution, StandardUniform};
 use core::fmt;
 use num_traits::{Float, FloatConst};
 use rand::Rng;
@@ -58,7 +58,7 @@ use rand::Rng;
 pub struct Cauchy<F>
 where
     F: Float + FloatConst,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     median: F,
     scale: F,
@@ -85,7 +85,7 @@ impl std::error::Error for Error {}
 impl<F> Cauchy<F>
 where
     F: Float + FloatConst,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     /// Construct a new `Cauchy` with the given shape parameters
     /// `median` the peak location and `scale` the scale factor.
@@ -100,11 +100,11 @@ where
 impl<F> Distribution<F> for Cauchy<F>
 where
     F: Float + FloatConst,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F {
         // sample from [0, 1)
-        let x = Standard.sample(rng);
+        let x = StandardUniform.sample(rng);
         // get standard cauchy random number
         // note that π/2 is not exactly representable, even if x=0.5 the result is finite
         let comp_dev = (F::PI() * x).tan();
@@ -166,7 +166,7 @@ mod test {
     fn value_stability() {
         fn gen_samples<F: Float + FloatConst + fmt::Debug>(m: F, s: F, buf: &mut [F])
         where
-            Standard: Distribution<F>,
+            StandardUniform: Distribution<F>,
         {
             let distr = Cauchy::new(m, s).unwrap();
             let mut rng = crate::test::rng(353);

rand_distr/src/inverse_gaussian.rs

@@ -1,6 +1,6 @@
 //! The inverse Gaussian distribution `IG(μ, λ)`.
 
-use crate::{Distribution, Standard, StandardNormal};
+use crate::{Distribution, StandardNormal, StandardUniform};
 use core::fmt;
 use num_traits::Float;
 use rand::Rng;
@@ -53,7 +53,7 @@ pub struct InverseGaussian<F>
 where
     F: Float,
     StandardNormal: Distribution<F>,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     mean: F,
     shape: F,
@@ -63,7 +63,7 @@ impl<F> InverseGaussian<F>
 where
     F: Float,
     StandardNormal: Distribution<F>,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     /// Construct a new `InverseGaussian` distribution with the given mean and
     /// shape.
@@ -85,7 +85,7 @@ impl<F> Distribution<F> for InverseGaussian<F>
 where
     F: Float,
     StandardNormal: Distribution<F>,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     #[allow(clippy::many_single_char_names)]
     fn sample<R>(&self, rng: &mut R) -> F

rand_distr/src/lib.rs

@@ -34,7 +34,7 @@
 //! The following are re-exported:
 //!
 //! - The [`Distribution`] trait and [`DistIter`] helper type
-//! - The [`Standard`], [`Alphanumeric`], [`Uniform`], [`OpenClosed01`],
+//! - The [`StandardUniform`], [`Alphanumeric`], [`Uniform`], [`OpenClosed01`],
 //!   [`Open01`], [`Bernoulli`], and [`WeightedIndex`] distributions
 //!
 //! ## Distributions
@@ -95,7 +95,7 @@ use rand::Rng;
 
 pub use rand::distr::{
     uniform, Alphanumeric, Bernoulli, BernoulliError, DistIter, Distribution, Open01, OpenClosed01,
-    Standard, Uniform,
+    StandardUniform, Uniform,
 };
 
 pub use self::beta::{Beta, Error as BetaError};

rand_distr/src/normal_inverse_gaussian.rs

@@ -1,4 +1,4 @@
-use crate::{Distribution, InverseGaussian, Standard, StandardNormal};
+use crate::{Distribution, InverseGaussian, StandardNormal, StandardUniform};
 use core::fmt;
 use num_traits::Float;
 use rand::Rng;
@@ -54,7 +54,7 @@ pub struct NormalInverseGaussian<F>
 where
     F: Float,
     StandardNormal: Distribution<F>,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     beta: F,
     inverse_gaussian: InverseGaussian<F>,
@@ -64,7 +64,7 @@ impl<F> NormalInverseGaussian<F>
 where
     F: Float,
     StandardNormal: Distribution<F>,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     /// Construct a new `NormalInverseGaussian` distribution with the given alpha (tail heaviness) and
     /// beta (asymmetry) parameters.
@@ -94,7 +94,7 @@ impl<F> Distribution<F> for NormalInverseGaussian<F>
 where
     F: Float,
     StandardNormal: Distribution<F>,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     fn sample<R>(&self, rng: &mut R) -> F
     where

rand_distr/src/poisson.rs

@@ -9,7 +9,7 @@
 
 //! The Poisson distribution `Poisson(λ)`.
 
-use crate::{Cauchy, Distribution, Standard};
+use crate::{Cauchy, Distribution, StandardUniform};
 use core::fmt;
 use num_traits::{Float, FloatConst};
 use rand::Rng;
@@ -55,7 +55,7 @@ use rand::Rng;
 pub struct Poisson<F>(Method<F>)
 where
     F: Float + FloatConst,
-    Standard: Distribution<F>;
+    StandardUniform: Distribution<F>;
 
 /// Error type returned from [`Poisson::new`].
 #[derive(Clone, Copy, Debug, PartialEq, Eq)]
@@ -130,7 +130,7 @@ enum Method<F> {
 impl<F> Poisson<F>
 where
     F: Float + FloatConst,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     /// Construct a new `Poisson` with the given shape parameter
     /// `lambda`.
@@ -172,7 +172,7 @@ where
 impl<F> Distribution<F> for KnuthMethod<F>
 where
     F: Float + FloatConst,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F {
         let mut result = F::one();
@@ -188,7 +188,7 @@ where
 impl<F> Distribution<F> for RejectionMethod<F>
 where
     F: Float + FloatConst,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F {
         // The algorithm from Numerical Recipes in C
@@ -238,7 +238,7 @@ where
 impl<F> Distribution<F> for Poisson<F>
 where
     F: Float + FloatConst,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     #[inline]
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F {
@@ -255,7 +255,7 @@ mod test {
 
     fn test_poisson_avg_gen<F: Float + FloatConst>(lambda: F, tol: F)
     where
-        Standard: Distribution<F>,
+        StandardUniform: Distribution<F>,
     {
         let poisson = Poisson::new(lambda).unwrap();
         let mut rng = crate::test::rng(123);

rand_distr/src/triangular.rs

@@ -7,7 +7,7 @@
 // except according to those terms.
 //! The triangular distribution.
 
-use crate::{Distribution, Standard};
+use crate::{Distribution, StandardUniform};
 use core::fmt;
 use num_traits::Float;
 use rand::Rng;
@@ -43,7 +43,7 @@ use rand::Rng;
 pub struct Triangular<F>
 where
     F: Float,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     min: F,
     max: F,
@@ -76,7 +76,7 @@ impl std::error::Error for TriangularError {}
 impl<F> Triangular<F>
 where
     F: Float,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     /// Set up the Triangular distribution with defined `min`, `max` and `mode`.
     #[inline]
@@ -94,11 +94,11 @@ where
 impl<F> Distribution<F> for Triangular<F>
 where
     F: Float,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
 {
     #[inline]
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F {
-        let f: F = rng.sample(Standard);
+        let f: F = rng.sample(StandardUniform);
         let diff_mode_min = self.mode - self.min;
         let range = self.max - self.min;
         let f_range = f * range;

rand_distr/src/zeta.rs

@@ -8,7 +8,7 @@
 
 //! The Zeta distribution.
 
-use crate::{Distribution, Standard};
+use crate::{Distribution, StandardUniform};
 use core::fmt;
 use num_traits::Float;
 use rand::{distr::OpenClosed01, Rng};
@@ -68,7 +68,7 @@ use rand::{distr::OpenClosed01, Rng};
 pub struct Zeta<F>
 where
     F: Float,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
     OpenClosed01: Distribution<F>,
 {
     s_minus_1: F,
@@ -96,7 +96,7 @@ impl std::error::Error for Error {}
 impl<F> Zeta<F>
 where
     F: Float,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
     OpenClosed01: Distribution<F>,
 {
     /// Construct a new `Zeta` distribution with given `s` parameter.
@@ -117,7 +117,7 @@ where
 impl<F> Distribution<F> for Zeta<F>
 where
     F: Float,
-    Standard: Distribution<F>,
+    StandardUniform: Distribution<F>,
     OpenClosed01: Distribution<F>,
 {
     #[inline]
@@ -135,7 +135,7 @@ where
 
             let t = (F::one() + F::one() / x).powf(self.s_minus_1);
 
-            let v = rng.sample(Standard);
+            let v = rng.sample(StandardUniform);
             if v * x * (t - F::one()) * self.b <= t * (self.b - F::one()) {
                 return x;
             }