Additional source-header notices, retained verbatim below their source locations.

Source: src/distributions/bernoulli.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! The Bernoulli distribution.


Source: src/distributions/distribution.rs:1
// Copyright 2018 Developers of the Rand project.
// Copyright 2013-2017 The Rust Project Developers.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Distribution trait and associates


Source: src/distributions/float.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Basic floating-point number distributions


Source: src/distributions/integer.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! The implementations of the `Standard` distribution for integer types.


Source: src/distributions/mod.rs:1
// Copyright 2018 Developers of the Rand project.
// Copyright 2013-2017 The Rust Project Developers.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Generating random samples from probability distributions
//!
//! 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
//! [`Rng::sample`].
//!
//! Abstractly, a [probability distribution] describes the probability of
//! occurrence of each value in its sample space.
//!
//! More concretely, an implementation of `Distribution<T>` for type `X` is an
//! algorithm for choosing values from the sample space (a subset of `T`)
//! according to the distribution `X` represents, using an external source of
//! randomness (an RNG supplied to the `sample` function).
//!
//! A type `X` may implement `Distribution<T>` for multiple types `T`.
//! Any type implementing [`Distribution`] is stateless (i.e. immutable),
//! but it may have internal parameters set at construction time (for example,
//! [`Uniform`] allows specification of its sample space as a range within `T`).
//!
//!
//! # The `Standard` distribution
//!
//! The [`Standard`] distribution is important to mention. This is the
//! distribution used by [`Rng::gen`] 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
//! with the [`random`] function.
//!
//! ## Random characters
//!
//! [`Alphanumeric`] is a simple distribution to sample random letters and
//! numbers of the `char` type; in contrast [`Standard`] may sample any valid
//! `char`.
//!
//!
//! # Uniform numeric ranges
//!
//! The [`Uniform`] distribution is more flexible than [`Standard`], but also
//! more specialised: it supports fewer target types, but allows the sample
//! 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::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
//! `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::sample(Range)`.
//!
//! ## Open and half-open ranges
//!
//! There are surprisingly many ways to uniformly generate random floats. A
//! range between 0 and 1 is standard, but the exact bounds (open vs closed)
//! and accuracy differ. In addition to the [`Standard`] distribution Rand offers
//! [`Open01`] and [`OpenClosed01`]. See "Floating point implementation" section of
//! [`Standard`] documentation for more details.
//!
//! # Non-uniform sampling
//!
//! Sampling a simple true/false outcome with a given probability has a name:
//! the [`Bernoulli`] distribution (this is used by [`Rng::gen_bool`]).
//!
//! For weighted sampling from a sequence of discrete values, use the
//! [`WeightedIndex`] distribution.
//!
//! This crate no longer includes other non-uniform distributions; instead
//! it is recommended that you use either [`rand_distr`] or [`statrs`].
//!
//!
//! [probability distribution]: https://en.wikipedia.org/wiki/Probability_distribution
//! [`rand_distr`]: https://crates.io/crates/rand_distr
//! [`statrs`]: https://crates.io/crates/statrs

//! [`random`]: crate::random
//! [`rand_distr`]: https://crates.io/crates/rand_distr
//! [`statrs`]: https://crates.io/crates/statrs


Source: src/distributions/other.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! The implementations of the `Standard` distribution for other built-in types.


Source: src/distributions/slice.rs:1
// Copyright 2021 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.


Source: src/distributions/uniform.rs:1
// 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
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! A distribution uniformly sampling numbers within a given range.
//!
//! [`Uniform`] is the standard distribution to sample uniformly from a range;
//! e.g. `Uniform::new_inclusive(1, 6)` can sample integers from 1 to 6, like a
//! standard die. [`Rng::gen_range`] supports any type supported by
//! [`Uniform`].
//!
//! This distribution is provided with support for several primitive types
//! (all integer and floating-point types) as well as [`std::time::Duration`],
//! and supports extension to user-defined types via a type-specific *back-end*
//! implementation.
//!
//! The types [`UniformInt`], [`UniformFloat`] and [`UniformDuration`] are the
//! back-ends supporting sampling from primitive integer and floating-point
//! ranges as well as from [`std::time::Duration`]; these types do not normally
//! need to be used directly (unless implementing a derived back-end).
//!
//! # Example usage
//!
//! ```
//! use rand::{Rng, thread_rng};
//! use rand::distributions::Uniform;
//!
//! let mut rng = thread_rng();
//! let side = Uniform::new(-10.0, 10.0);
//!
//! // sample between 1 and 10 points
//! 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);
//! }
//! ```
//!
//! # Extending `Uniform` to support a custom type
//!
//! To extend [`Uniform`] to support your own types, write a back-end which
//! implements the [`UniformSampler`] trait, then implement the [`SampleUniform`]
//! helper trait to "register" your back-end. See the `MyF32` example below.
//!
//! At a minimum, the back-end needs to store any parameters needed for sampling
//! (e.g. the target range) and implement `new`, `new_inclusive` and `sample`.
//! Those methods should include an assert to check the range is valid (i.e.
//! `low < high`). The example below merely wraps another back-end.
//!
//! The `new`, `new_inclusive` and `sample_single` functions use arguments of
//! type `SampleBorrow<X>` in order to support passing in values by reference or
//! by value. In the implementation of these functions, you can choose to
//! simply use the reference returned by [`SampleBorrow::borrow`], or you can choose
//! to copy or clone the value, whatever is appropriate for your type.
//!
//! ```
//! use rand::prelude::*;
//! use rand::distributions::uniform::{Uniform, SampleUniform,
//!         UniformSampler, UniformFloat, SampleBorrow};
//!
//! struct MyF32(f32);
//!
//! #[derive(Clone, Copy, Debug)]
//! struct UniformMyF32(UniformFloat<f32>);
//!
//! impl UniformSampler for UniformMyF32 {
//!     type X = MyF32;
//!     fn new<B1, B2>(low: B1, high: B2) -> Self
//!         where B1: SampleBorrow<Self::X> + Sized,
//!               B2: SampleBorrow<Self::X> + Sized
//!     {
//!         UniformMyF32(UniformFloat::<f32>::new(low.borrow().0, high.borrow().0))
//!     }
//!     fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self
//!         where B1: SampleBorrow<Self::X> + Sized,
//!               B2: SampleBorrow<Self::X> + Sized
//!     {
//!         UniformMyF32(UniformFloat::<f32>::new_inclusive(
//!             low.borrow().0,
//!             high.borrow().0,
//!         ))
//!     }
//!     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
//!         MyF32(self.0.sample(rng))
//!     }
//! }
//!
//! impl SampleUniform for MyF32 {
//!     type Sampler = UniformMyF32;
//! }
//!
//! let (low, high) = (MyF32(17.0f32), MyF32(22.0f32));
//! let uniform = Uniform::new(low, high);
//! let x = uniform.sample(&mut thread_rng());
//! ```
//!
//! [`SampleUniform`]: crate::distributions::uniform::SampleUniform
//! [`UniformSampler`]: crate::distributions::uniform::UniformSampler
//! [`UniformInt`]: crate::distributions::uniform::UniformInt
//! [`UniformFloat`]: crate::distributions::uniform::UniformFloat
//! [`UniformDuration`]: crate::distributions::uniform::UniformDuration
//! [`SampleBorrow::borrow`]: crate::distributions::uniform::SampleBorrow::borrow


Source: src/distributions/utils.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Math helper functions



Source: src/distributions/weighted.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Weighted index sampling
//!
//! This module is deprecated. Use [`crate::distributions::WeightedIndex`] and
//! [`crate::distributions::WeightedError`] instead.


Source: src/distributions/weighted_index.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Weighted index sampling


Source: src/lib.rs:1
// Copyright 2018 Developers of the Rand project.
// Copyright 2013-2017 The Rust Project Developers.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Utilities for random number generation
//!
//! Rand provides utilities to generate random numbers, to convert them to
//! useful types and distributions, and some randomness-related algorithms.
//!
//! # Quick Start
//!
//! To get you started quickly, the easiest and highest-level way to get
//! a random value is to use [`random()`]; alternatively you can use
//! [`thread_rng()`]. The [`Rng`] trait provides a useful API on all RNGs, while
//! the [`distributions`] and [`seq`] modules provide further
//! functionality on top of RNGs.
//!
//! ```
//! use rand::prelude::*;
//!
//! if rand::random() { // generates a boolean
//!     // Try printing a random unicode code point (probably a bad idea)!
//!     println!("char: {}", rand::random::<char>());
//! }
//!
//! let mut rng = rand::thread_rng();
//! let y: f64 = rng.gen(); // generates a float between 0 and 1
//!
//! let mut nums: Vec<i32> = (1..100).collect();
//! nums.shuffle(&mut rng);
//! ```
//!
//! # The Book
//!
//! For the user guide and further documentation, please read
//! [The Rust Rand Book](https://rust-random.github.io/book).


Source: src/prelude.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Convenience re-export of common members
//!
//! Like the standard library's prelude, this module simplifies importing of
//! common items. Unlike the standard prelude, the contents of this module must
//! be imported manually:
//!
//! ```
//! use rand::prelude::*;
//! # let mut r = StdRng::from_rng(thread_rng()).unwrap();
//! # let _: f32 = r.gen();
//! ```


Source: src/rng.rs:1
// Copyright 2018 Developers of the Rand project.
// Copyright 2013-2017 The Rust Project Developers.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! [`Rng`] trait


Source: src/rngs/adapter/mod.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Wrappers / adapters forming RNGs


Source: src/rngs/adapter/read.rs:1
// Copyright 2018 Developers of the Rand project.
// Copyright 2013 The Rust Project Developers.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! A wrapper around any Read to treat it as an RNG.


Source: src/rngs/adapter/reseeding.rs:1
// Copyright 2018 Developers of the Rand project.
// Copyright 2013 The Rust Project Developers.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! A wrapper around another PRNG that reseeds it after it
//! generates a certain number of random bytes.


Source: src/rngs/mock.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Mock random number generator


Source: src/rngs/mod.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Random number generators and adapters
//!
//! ## Background: Random number generators (RNGs)
//!
//! Computers cannot produce random numbers from nowhere. We classify
//! random number generators as follows:
//!
//! -   "True" random number generators (TRNGs) use hard-to-predict data sources
//!     (e.g. the high-resolution parts of event timings and sensor jitter) to
//!     harvest random bit-sequences, apply algorithms to remove bias and
//!     estimate available entropy, then combine these bits into a byte-sequence
//!     or an entropy pool. This job is usually done by the operating system or
//!     a hardware generator (HRNG).
//! -   "Pseudo"-random number generators (PRNGs) use algorithms to transform a
//!     seed into a sequence of pseudo-random numbers. These generators can be
//!     fast and produce well-distributed unpredictable random numbers (or not).
//!     They are usually deterministic: given algorithm and seed, the output
//!     sequence can be reproduced. They have finite period and eventually loop;
//!     with many algorithms this period is fixed and can be proven sufficiently
//!     long, while others are chaotic and the period depends on the seed.
//! -   "Cryptographically secure" pseudo-random number generators (CSPRNGs)
//!     are the sub-set of PRNGs which are secure. Security of the generator
//!     relies both on hiding the internal state and using a strong algorithm.
//!
//! ## Traits and functionality
//!
//! All RNGs implement the [`RngCore`] trait, as a consequence of which the
//! [`Rng`] extension trait is automatically implemented. Secure RNGs may
//! additionally implement the [`CryptoRng`] trait.
//!
//! All PRNGs require a seed to produce their random number sequence. The
//! [`SeedableRng`] trait provides three ways of constructing PRNGs:
//!
//! -   `from_seed` accepts a type specific to the PRNG
//! -   `from_rng` allows a PRNG to be seeded from any other RNG
//! -   `seed_from_u64` allows any PRNG to be seeded from a `u64` insecurely
//! -   `from_entropy` securely seeds a PRNG from fresh entropy
//!
//! Use the [`rand_core`] crate when implementing your own RNGs.
//!
//! ## Our generators
//!
//! This crate provides several random number generators:
//!
//! -   [`OsRng`] is an interface to the operating system's random number
//!     source. Typically the operating system uses a CSPRNG with entropy
//!     provided by a TRNG and some type of on-going re-seeding.
//! -   [`ThreadRng`], provided by the [`thread_rng`] function, is a handle to a
//!     thread-local CSPRNG with periodic seeding from [`OsRng`]. Because this
//!     is local, it is typically much faster than [`OsRng`]. It should be
//!     secure, though the paranoid may prefer [`OsRng`].
//! -   [`StdRng`] is a CSPRNG chosen for good performance and trust of security
//!     (based on reviews, maturity and usage). The current algorithm is ChaCha12,
//!     which is well established and rigorously analysed.
//!     [`StdRng`] provides the algorithm used by [`ThreadRng`] but without
//!     periodic reseeding.
//! -   [`SmallRng`] is an **insecure** PRNG designed to be fast, simple, require
//!     little memory, and have good output quality.
//!
//! The algorithms selected for [`StdRng`] and [`SmallRng`] may change in any
//! release and may be platform-dependent, therefore they should be considered
//! **not reproducible**.
//!
//! ## Additional generators
//!
//! **TRNGs**: The [`rdrand`] crate provides an interface to the RDRAND and
//! RDSEED instructions available in modern Intel and AMD CPUs.
//! The [`rand_jitter`] crate provides a user-space implementation of
//! entropy harvesting from CPU timer jitter, but is very slow and has
//! [security issues](https://github.com/rust-random/rand/issues/699).
//!
//! **PRNGs**: Several companion crates are available, providing individual or
//! families of PRNG algorithms. These provide the implementations behind
//! [`StdRng`] and [`SmallRng`] but can also be used directly, indeed *should*
//! be used directly when **reproducibility** matters.
//! Some suggestions are: [`rand_chacha`], [`rand_pcg`], [`rand_xoshiro`].
//! A full list can be found by searching for crates with the [`rng` tag].
//!
//! [`Rng`]: crate::Rng
//! [`RngCore`]: crate::RngCore
//! [`CryptoRng`]: crate::CryptoRng
//! [`SeedableRng`]: crate::SeedableRng
//! [`thread_rng`]: crate::thread_rng
//! [`rdrand`]: https://crates.io/crates/rdrand
//! [`rand_jitter`]: https://crates.io/crates/rand_jitter
//! [`rand_chacha`]: https://crates.io/crates/rand_chacha
//! [`rand_pcg`]: https://crates.io/crates/rand_pcg
//! [`rand_xoshiro`]: https://crates.io/crates/rand_xoshiro
//! [`rng` tag]: https://crates.io/keywords/rng


Source: src/rngs/small.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! A small fast RNG


Source: src/rngs/std.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! The standard RNG


Source: src/rngs/thread.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Thread-local random number generator


Source: src/rngs/xoshiro128plusplus.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.


Source: src/rngs/xoshiro256plusplus.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.


Source: src/seq/index.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Low-level API for sampling indices


Source: src/seq/mod.rs:1
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Sequence-related functionality
//!
//! This module provides:
//!
//! *   [`SliceRandom`] slice sampling and mutation
//! *   [`IteratorRandom`] iterator sampling
//! *   [`index::sample`] low-level API to choose multiple indices from
//!     `0..length`
//!
//! Also see:
//!
//! *   [`crate::distributions::WeightedIndex`] distribution which provides
//!     weighted index sampling.
//!
//! In order to make results reproducible across 32-64 bit architectures, all
//! `usize` indices are sampled as a `u32` where possible (also providing a
//! small performance boost in some cases).



