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diff --git a/vendor/rand/src/distributions/mod.rs b/vendor/rand/src/distributions/mod.rs new file mode 100644 index 000000000..05ca80606 --- /dev/null +++ b/vendor/rand/src/distributions/mod.rs @@ -0,0 +1,218 @@ +// 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 + +mod bernoulli; +mod distribution; +mod float; +mod integer; +mod other; +mod slice; +mod utils; +#[cfg(feature = "alloc")] +mod weighted_index; + +#[doc(hidden)] +pub mod hidden_export { + pub use super::float::IntoFloat; // used by rand_distr +} +pub mod uniform; +#[deprecated( + since = "0.8.0", + note = "use rand::distributions::{WeightedIndex, WeightedError} instead" +)] +#[cfg(feature = "alloc")] +#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))] +pub mod weighted; + +pub use self::bernoulli::{Bernoulli, BernoulliError}; +pub use self::distribution::{Distribution, DistIter, DistMap}; +#[cfg(feature = "alloc")] +pub use self::distribution::DistString; +pub use self::float::{Open01, OpenClosed01}; +pub use self::other::Alphanumeric; +pub use self::slice::Slice; +#[doc(inline)] +pub use self::uniform::Uniform; +#[cfg(feature = "alloc")] +pub use self::weighted_index::{WeightedError, WeightedIndex}; + +#[allow(unused)] +use crate::Rng; + +/// A generic random value distribution, implemented for many primitive types. +/// Usually generates values with a numerically uniform distribution, and with a +/// range appropriate to the type. +/// +/// ## Provided implementations +/// +/// Assuming the provided `Rng` is well-behaved, these implementations +/// generate values with the following ranges and distributions: +/// +/// * Integers (`i32`, `u32`, `isize`, `usize`, etc.): Uniformly distributed +/// over all values of the type. +/// * `char`: Uniformly distributed over all Unicode scalar values, i.e. all +/// code points in the range `0...0x10_FFFF`, except for the range +/// `0xD800...0xDFFF` (the surrogate code points). This includes +/// unassigned/reserved code points. +/// * `bool`: Generates `false` or `true`, each with probability 0.5. +/// * Floating point types (`f32` and `f64`): Uniformly distributed in the +/// half-open range `[0, 1)`. See notes below. +/// * Wrapping integers (`Wrapping<T>`), besides the type identical to their +/// normal integer variants. +/// +/// The `Standard` distribution also supports generation of the following +/// compound types where all component types are supported: +/// +/// * Tuples (up to 12 elements): each element is generated sequentially. +/// * Arrays (up to 32 elements): each element is generated sequentially; +/// see also [`Rng::fill`] which supports arbitrary array length for integer +/// and float types and tends to be faster for `u32` and smaller types. +/// When using `rustc` ≥ 1.51, enable the `min_const_gen` feature to support +/// arrays larger than 32 elements. +/// Note that [`Rng::fill`] and `Standard`'s array support are *not* equivalent: +/// the former is optimised for integer types (using fewer RNG calls for +/// element types smaller than the RNG word size), while the latter supports +/// any element type supported by `Standard`. +/// * `Option<T>` first generates a `bool`, and if true generates and returns +/// `Some(value)` where `value: T`, otherwise returning `None`. +/// +/// ## Custom implementations +/// +/// The [`Standard`] distribution may be implemented for user types as follows: +/// +/// ``` +/// # #![allow(dead_code)] +/// use rand::Rng; +/// use rand::distributions::{Distribution, Standard}; +/// +/// struct MyF32 { +/// x: f32, +/// } +/// +/// impl Distribution<MyF32> for Standard { +/// fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> MyF32 { +/// MyF32 { x: rng.gen() } +/// } +/// } +/// ``` +/// +/// ## Example usage +/// ``` +/// use rand::prelude::*; +/// use rand::distributions::Standard; +/// +/// let val: f32 = StdRng::from_entropy().sample(Standard); +/// println!("f32 from [0, 1): {}", val); +/// ``` +/// +/// # Floating point implementation +/// The floating point implementations for `Standard` generate a random value in +/// the half-open interval `[0, 1)`, i.e. including 0 but not 1. +/// +/// All values that can be generated are of the form `n * ε/2`. For `f32` +/// the 24 most significant random bits of a `u32` are used and for `f64` the +/// 53 most significant bits of a `u64` are used. The conversion uses the +/// 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` 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)] +#[cfg_attr(feature = "serde1", derive(serde::Serialize, serde::Deserialize))] +pub struct Standard; |