Rename rand::distributions -> rand::distr

Author: dhardy

README.md

@@ -17,7 +17,7 @@ A Rust library for random number generation, featuring:
     ([see the book](https://rust-random.github.io/book/crates.html))
 -   Fast implementations of the best-in-class [cryptographic](https://rust-random.github.io/book/guide-rngs.html#cryptographically-secure-pseudo-random-number-generators-csprngs) and
     [non-cryptographic](https://rust-random.github.io/book/guide-rngs.html#basic-pseudo-random-number-generators-prngs) generators
--   A flexible [`distributions`](https://docs.rs/rand/*/rand/distributions/index.html) module
+-   A flexible [`distributions`](https://docs.rs/rand/*/rand/distr/index.html) module
 -   Samplers for a large number of random number distributions via our own
     [`rand_distr`](https://docs.rs/rand_distr) and via
     the [`statrs`](https://docs.rs/statrs/0.13.0/statrs/)

benches/Cargo.toml

@@ -15,8 +15,8 @@ criterion = "0.5"
 criterion-cycles-per-byte = "0.6"
 
 [[bench]]
-name = "distributions"
-path = "src/distributions.rs"
+name = "distr"
+path = "src/distr.rs"
 harness = false
 
 [[bench]]

benches/benches/base_distributions.rs

@@ -16,8 +16,8 @@ extern crate test;
 
 const RAND_BENCH_N: u64 = 1000;
 
-use rand::distributions::{Alphanumeric, Open01, OpenClosed01, Standard, Uniform};
-use rand::distributions::uniform::{UniformInt, UniformSampler};
+use rand::distr::{Alphanumeric, Open01, OpenClosed01, Standard, Uniform};
+use rand::distr::uniform::{UniformInt, UniformSampler};
 use core::mem::size_of;
 use core::num::{NonZeroU128, NonZeroU16, NonZeroU32, NonZeroU64, NonZeroU8};
 use core::time::Duration;
@@ -253,7 +253,7 @@ gen_range_float!(gen_range_f32, f32, -20000.0f32, 100000.0);
 gen_range_float!(gen_range_f64, f64, 123.456f64, 7890.12);
 
 
-// In src/distributions/uniform.rs, we say:
+// In src/distr/uniform.rs, we say:
 // Implementation of [`uniform_single`] is optional, and is only useful when
 // the implementation can be faster than `Self::new(low, high).sample(rng)`.
 

benches/benches/misc.rs

@@ -14,7 +14,7 @@ const RAND_BENCH_N: u64 = 1000;
 
 use test::Bencher;
 
-use rand::distributions::{Bernoulli, Distribution, Standard};
+use rand::distr::{Bernoulli, Distribution, Standard};
 use rand::prelude::*;
 use rand_pcg::{Pcg32, Pcg64Mcg};
 

benches/benches/weighted.rs

@@ -10,7 +10,7 @@
 
 extern crate test;
 
-use rand::distributions::WeightedIndex;
+use rand::distr::WeightedIndex;
 use rand::Rng;
 use test::Bencher;
 

benches/src/distributions.rs renamed to benches/src/distr.rs

@@ -10,7 +10,7 @@
 
 use core::time::Duration;
 use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
-use rand::distributions::uniform::{SampleRange, Uniform};
+use rand::distr::uniform::{SampleRange, Uniform};
 use rand::prelude::*;
 use rand_chacha::ChaCha8Rng;
 use rand_pcg::{Pcg32, Pcg64};

benches/src/uniform.rs

@@ -14,7 +14,7 @@
 
 use core::time::Duration;
 use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
-use rand::distributions::uniform::{SampleUniform, Uniform, UniformSampler};
+use rand::distr::uniform::{SampleUniform, Uniform, UniformSampler};
 use rand::prelude::*;
 use rand_chacha::ChaCha8Rng;
 use rand_pcg::{Pcg32, Pcg64};

benches/src/uniform_float.rs

@@ -23,7 +23,7 @@
 //! We can use the above fact to estimate the value of π: pick many points in
 //! the square at random, calculate the fraction that fall within the circle,
 //! and multiply this fraction by 4.
-use rand::distributions::{Distribution, Uniform};
+use rand::distr::{Distribution, Uniform};
 
 fn main() {
     let range = Uniform::new(-1.0f64, 1.0).unwrap();

examples/monte-carlo.rs

@@ -26,7 +26,7 @@
 //!
 //! [Monty Hall Problem]: https://en.wikipedia.org/wiki/Monty_Hall_problem
 
-use rand::distributions::{Distribution, Uniform};
+use rand::distr::{Distribution, Uniform};
 use rand::Rng;
 
 struct SimulationResult {

examples/monty-hall.rs

@@ -38,7 +38,7 @@
 //! over BATCH_SIZE trials. Manually batching also turns out to be faster
 //! for the nondeterministic version of this program as well.
 
-use rand::distributions::{Distribution, Uniform};
+use rand::distr::{Distribution, Uniform};
 use rand_chacha::{rand_core::SeedableRng, ChaCha8Rng};
 use rayon::prelude::*;
 

examples/rayon-monte-carlo.rs

@@ -8,7 +8,7 @@
 
 Implements a full suite of random number distribution sampling routines.
 
-This crate is a superset of the [rand::distributions] module, including support
+This crate is a superset of the [rand::distr] module, including support
 for sampling from Beta, Binomial, Cauchy, ChiSquared, Dirichlet, Exponential,
 FisherF, Gamma, Geometric, Hypergeometric, InverseGaussian, LogNormal, Normal,
 Pareto, PERT, Poisson, StudentT, Triangular and Weibull distributions.  Sampling
@@ -46,7 +46,7 @@ can be enabled. (Note that any other crate depending on `num-traits` with the
 
 
 [statrs]: https://github.com/boxtown/statrs
-[rand::distributions]: https://rust-random.github.io/rand/rand/distributions/index.html
+[rand::distr]: https://rust-random.github.io/rand/rand/distr/index.html
 
 ## License
 

rand_distr/README.md

@@ -4,7 +4,7 @@ use crate::Distribution;
 use core::fmt;
 #[allow(unused_imports)]
 use num_traits::Float;
-use rand::distributions::uniform::Uniform;
+use rand::distr::uniform::Uniform;
 use rand::Rng;
 
 #[derive(Clone, Copy, Debug, PartialEq)]

rand_distr/src/hypergeometric.rs

@@ -27,8 +27,8 @@
 //!
 //! ## Re-exports
 //!
-//! This crate is a super-set of the [`rand::distributions`] module. See the
-//! [`rand::distributions`] module documentation for an overview of the core
+//! This crate is a super-set of the [`rand::distr`] module. See the
+//! [`rand::distr`] module documentation for an overview of the core
 //! [`Distribution`] trait and implementations.
 //!
 //! The following are re-exported:
@@ -93,7 +93,7 @@ extern crate std;
 #[allow(unused)]
 use rand::Rng;
 
-pub use rand::distributions::{
+pub use rand::distr::{
     uniform, Alphanumeric, Bernoulli, BernoulliError, DistIter, Distribution, Open01, OpenClosed01,
     Standard, Uniform,
 };
@@ -129,7 +129,7 @@ pub use self::weibull::{Error as WeibullError, Weibull};
 pub use self::zeta::{Error as ZetaError, Zeta};
 pub use self::zipf::{Error as ZipfError, Zipf};
 #[cfg(feature = "alloc")]
-pub use rand::distributions::{WeightError, WeightedIndex};
+pub use rand::distr::{WeightError, WeightedIndex};
 pub use student_t::StudentT;
 #[cfg(feature = "alloc")]
 pub use weighted_alias::WeightedAliasIndex;

rand_distr/src/lib.rs

@@ -10,7 +10,7 @@
 
 use crate::ziggurat_tables;
 use num_traits::Float;
-use rand::distributions::hidden_export::IntoFloat;
+use rand::distr::hidden_export::IntoFloat;
 use rand::Rng;
 
 /// Calculates ln(gamma(x)) (natural logarithm of the gamma

rand_distr/src/utils.rs

@@ -14,8 +14,8 @@ use core::ops::SubAssign;
 use super::WeightError;
 use crate::Distribution;
 use alloc::vec::Vec;
-use rand::distributions::uniform::{SampleBorrow, SampleUniform};
-use rand::distributions::Weight;
+use rand::distr::uniform::{SampleBorrow, SampleUniform};
+use rand::distr::Weight;
 use rand::Rng;
 #[cfg(feature = "serde1")]
 use serde::{Deserialize, Serialize};
@@ -30,7 +30,7 @@ use serde::{Deserialize, Serialize};
 ///
 /// # Key differences
 ///
-/// The main distinction between [`WeightedTreeIndex<W>`] and [`rand::distributions::WeightedIndex<W>`]
+/// The main distinction between [`WeightedTreeIndex<W>`] and [`rand::distr::WeightedIndex<W>`]
 /// lies in the internal representation of weights. In [`WeightedTreeIndex<W>`],
 /// weights are structured as a tree, which is optimized for frequent updates of the weights.
 ///

rand_distr/src/weighted_tree.rs

@@ -11,7 +11,7 @@
 use crate::{Distribution, Standard};
 use core::fmt;
 use num_traits::Float;
-use rand::{distributions::OpenClosed01, Rng};
+use rand::{distr::OpenClosed01, Rng};
 
 /// The [Zeta distribution](https://en.wikipedia.org/wiki/Zeta_distribution) `Zeta(s)`.
 ///

rand_distr/src/zeta.rs

@@ -8,7 +8,7 @@
 
 //! The Bernoulli distribution `Bernoulli(p)`.
 
-use crate::distributions::Distribution;
+use crate::distr::Distribution;
 use crate::Rng;
 use core::fmt;
 
@@ -31,7 +31,7 @@ use serde::{Deserialize, Serialize};
 /// # Example
 ///
 /// ```rust
-/// use rand::distributions::{Bernoulli, Distribution};
+/// use rand::distr::{Bernoulli, Distribution};
 ///
 /// let d = Bernoulli::new(0.3).unwrap();
 /// let v = d.sample(&mut rand::thread_rng());
@@ -153,7 +153,7 @@ impl Distribution<bool> for Bernoulli {
 #[cfg(test)]
 mod test {
     use super::Bernoulli;
-    use crate::distributions::Distribution;
+    use crate::distr::Distribution;
     use crate::Rng;
 
     #[test]

src/distributions/bernoulli.rs renamed to src/distr/bernoulli.rs

@@ -49,7 +49,7 @@ pub trait Distribution<T> {
     ///
     /// ```
     /// use rand::thread_rng;
-    /// use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard};
+    /// use rand::distr::{Distribution, Alphanumeric, Uniform, Standard};
     ///
     /// let mut rng = thread_rng();
     ///
@@ -89,7 +89,7 @@ pub trait Distribution<T> {
     ///
     /// ```
     /// use rand::thread_rng;
-    /// use rand::distributions::{Distribution, Uniform};
+    /// use rand::distr::{Distribution, Uniform};
     ///
     /// let mut rng = thread_rng();
     ///
@@ -201,12 +201,12 @@ pub trait DistString {
 
 #[cfg(test)]
 mod tests {
-    use crate::distributions::{Distribution, Uniform};
+    use crate::distr::{Distribution, Uniform};
     use crate::Rng;
 
     #[test]
     fn test_distributions_iter() {
-        use crate::distributions::Open01;
+        use crate::distr::Open01;
         let mut rng = crate::test::rng(210);
         let distr = Open01;
         let mut iter = Distribution::<f32>::sample_iter(distr, &mut rng);
@@ -248,7 +248,7 @@ mod tests {
     #[test]
     #[cfg(feature = "alloc")]
     fn test_dist_string() {
-        use crate::distributions::{Alphanumeric, DistString, Standard};
+        use crate::distr::{Alphanumeric, DistString, Standard};
         use core::str;
         let mut rng = crate::test::rng(213);
 

src/distributions/distribution.rs renamed to src/distr/distribution.rs

@@ -8,8 +8,8 @@
 
 //! Basic floating-point number distributions
 
-use crate::distributions::utils::{FloatAsSIMD, FloatSIMDUtils, IntAsSIMD};
-use crate::distributions::{Distribution, Standard};
+use crate::distr::utils::{FloatAsSIMD, FloatSIMDUtils, IntAsSIMD};
+use crate::distr::{Distribution, Standard};
 use crate::Rng;
 use core::mem;
 #[cfg(feature = "simd_support")]
@@ -33,15 +33,15 @@ use serde::{Deserialize, Serialize};
 /// # Example
 /// ```
 /// use rand::{thread_rng, Rng};
-/// use rand::distributions::OpenClosed01;
+/// use rand::distr::OpenClosed01;
 ///
 /// let val: f32 = thread_rng().sample(OpenClosed01);
 /// println!("f32 from (0, 1): {}", val);
 /// ```
 ///
-/// [`Standard`]: crate::distributions::Standard
-/// [`Open01`]: crate::distributions::Open01
-/// [`Uniform`]: crate::distributions::uniform::Uniform
+/// [`Standard`]: crate::distr::Standard
+/// [`Open01`]: crate::distr::Open01
+/// [`Uniform`]: crate::distr::uniform::Uniform
 #[derive(Clone, Copy, Debug)]
 #[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
 pub struct OpenClosed01;
@@ -60,15 +60,15 @@ pub struct OpenClosed01;
 /// # Example
 /// ```
 /// use rand::{thread_rng, Rng};
-/// use rand::distributions::Open01;
+/// use rand::distr::Open01;
 ///
 /// let val: f32 = thread_rng().sample(Open01);
 /// println!("f32 from (0, 1): {}", val);
 /// ```
 ///
-/// [`Standard`]: crate::distributions::Standard
-/// [`OpenClosed01`]: crate::distributions::OpenClosed01
-/// [`Uniform`]: crate::distributions::uniform::Uniform
+/// [`Standard`]: crate::distr::Standard
+/// [`OpenClosed01`]: crate::distr::OpenClosed01
+/// [`Uniform`]: crate::distr::uniform::Uniform
 #[derive(Clone, Copy, Debug)]
 #[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
 pub struct Open01;

src/distributions/float.rs renamed to src/distr/float.rs

@@ -8,7 +8,7 @@
 
 //! The implementations of the `Standard` distribution for integer types.
 
-use crate::distributions::{Distribution, Standard};
+use crate::distr::{Distribution, Standard};
 use crate::Rng;
 #[cfg(all(target_arch = "x86", feature = "simd_support"))]
 use core::arch::x86::__m512i;

src/distributions/integer.rs renamed to src/distr/integer.rs

@@ -172,7 +172,7 @@ use crate::Rng;
 /// ```
 /// # #![allow(dead_code)]
 /// use rand::Rng;
-/// use rand::distributions::{Distribution, Standard};
+/// use rand::distr::{Distribution, Standard};
 ///
 /// struct MyF32 {
 ///     x: f32,
@@ -188,7 +188,7 @@ use crate::Rng;
 /// ## Example usage
 /// ```
 /// use rand::prelude::*;
-/// use rand::distributions::Standard;
+/// use rand::distr::Standard;
 ///
 /// let val: f32 = StdRng::from_os_rng().sample(Standard);
 /// println!("f32 from [0, 1): {}", val);

src/distributions/mod.rs renamed to src/distr/mod.rs

@@ -14,8 +14,8 @@ use core::char;
 use core::num::Wrapping;
 
 #[cfg(feature = "alloc")]
-use crate::distributions::DistString;
-use crate::distributions::{Distribution, Standard, Uniform};
+use crate::distr::DistString;
+use crate::distr::{Distribution, Standard, Uniform};
 use crate::Rng;
 
 use core::mem::{self, MaybeUninit};
@@ -35,7 +35,7 @@ use serde::{Deserialize, Serialize};
 ///
 /// ```
 /// use rand::{Rng, thread_rng};
-/// use rand::distributions::Alphanumeric;
+/// use rand::distr::Alphanumeric;
 ///
 /// let mut rng = thread_rng();
 /// let chars: String = (0..7).map(|_| rng.sample(Alphanumeric) as char).collect();
@@ -45,7 +45,7 @@ use serde::{Deserialize, Serialize};
 /// The [`DistString`] trait provides an easier method of generating
 /// a random `String`, and offers more efficient allocation:
 /// ```
-/// use rand::distributions::{Alphanumeric, DistString};
+/// use rand::distr::{Alphanumeric, DistString};
 /// let string = Alphanumeric.sample_string(&mut rand::thread_rng(), 16);
 /// println!("Random string: {}", string);
 /// ```