# mod.rs - source
Source of the Rust file `/home/key/.cargo/registry/src/github.com-1ecc6299db9ec823/rand-0.8.5/src/distributions/mod.rs`.
Keywords: rust, rustlang, rust-lang

    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    31
    32
    33
    34
    35
    36
    37
    38
    39
    40
    41
    42
    43
    44
    45
    46
    47
    48
    49
    50
    51
    52
    53
    54
    55
    56
    57
    58
    59
    60
    61
    62
    63
    64
    65
    66
    67
    68
    69
    70
    71
    72
    73
    74
    75
    76
    77
    78
    79
    80
    81
    82
    83
    84
    85
    86
    87
    88
    89
    90
    91
    92
    93
    94
    95
    96
    97
    98
    99
    100
    101
    102
    103
    104
    105
    106
    107
    108
    109
    110
    111
    112
    113
    114
    115
    116
    117
    118
    119
    120
    121
    122
    123
    124
    125
    126
    127
    128
    129
    130
    131
    132
    133
    134
    135
    136
    137
    138
    139
    140
    141
    142
    143
    144
    145
    146
    147
    148
    149
    150
    151
    152
    153
    154
    155
    156
    157
    158
    159
    160
    161
    162
    163
    164
    165
    166
    167
    168
    169
    170
    171
    172
    173
    174
    175
    176
    177
    178
    179
    180
    181
    182
    183
    184
    185
    186
    187
    188
    189
    190
    191
    192
    193
    194
    195
    196
    197
    198
    199
    200
    201
    202
    203
    204
    205
    206
    207
    208
    209
    210
    211
    212
    213
    214
    215
    216
    217
    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;