| // 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, including [`gen`], [`gen_range`] and |
| //! of course [`sample`]. |
| //! |
| //! Abstractly, a [probability distribution] describes the probability of |
| //! occurance 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. |
| //! |
| //! |
| //! # Distribution to sample from a `Uniform` range |
| //! |
| //! 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::gen_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 |
| //! distribution object than to call [`Rng::gen_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::gen_range`]. |
| //! |
| //! |
| //! # Other distributions |
| //! |
| //! 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. |
| //! |
| //! [`Alphanumeric`] is a simple distribution to sample random letters and |
| //! numbers of the `char` type; in contrast [`Standard`] may sample any valid |
| //! `char`. |
| //! |
| //! [`WeightedIndex`] can be used to do weighted sampling from a set of items, |
| //! such as from an array. |
| //! |
| //! # Non-uniform probability distributions |
| //! |
| //! Rand currently provides the following probability distributions: |
| //! |
| //! - Related to real-valued quantities that grow linearly |
| //! (e.g. errors, offsets): |
| //! - [`Normal`] distribution, and [`StandardNormal`] as a primitive |
| //! - [`Cauchy`] distribution |
| //! - Related to Bernoulli trials (yes/no events, with a given probability): |
| //! - [`Binomial`] distribution |
| //! - [`Bernoulli`] distribution, similar to [`Rng::gen_bool`]. |
| //! - Related to positive real-valued quantities that grow exponentially |
| //! (e.g. prices, incomes, populations): |
| //! - [`LogNormal`] distribution |
| //! - Related to the occurrence of independent events at a given rate: |
| //! - [`Pareto`] distribution |
| //! - [`Poisson`] distribution |
| //! - [`Exp`]onential distribution, and [`Exp1`] as a primitive |
| //! - [`Weibull`] distribution |
| //! - Gamma and derived distributions: |
| //! - [`Gamma`] distribution |
| //! - [`ChiSquared`] distribution |
| //! - [`StudentT`] distribution |
| //! - [`FisherF`] distribution |
| //! - Triangular distribution: |
| //! - [`Beta`] distribution |
| //! - [`Triangular`] distribution |
| //! - Multivariate probability distributions |
| //! - [`Dirichlet`] distribution |
| //! - [`UnitSphereSurface`] distribution |
| //! - [`UnitCircle`] distribution |
| //! |
| //! # Examples |
| //! |
| //! Sampling from a distribution: |
| //! |
| //! ``` |
| //! use rand::{thread_rng, Rng}; |
| //! use rand::distributions::Exp; |
| //! |
| //! let exp = Exp::new(2.0); |
| //! let v = thread_rng().sample(exp); |
| //! println!("{} is from an Exp(2) distribution", v); |
| //! ``` |
| //! |
| //! Implementing the [`Standard`] distribution for a user type: |
| //! |
| //! ``` |
| //! # #![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() } |
| //! } |
| //! } |
| //! ``` |
| //! |
| //! |
| //! [probability distribution]: https://en.wikipedia.org/wiki/Probability_distribution |
| //! [`gen_range`]: Rng::gen_range |
| //! [`gen`]: Rng::gen |
| //! [`sample`]: Rng::sample |
| //! [`new_inclusive`]: Uniform::new_inclusive |
| //! [`Alphanumeric`]: distributions::Alphanumeric |
| //! [`Bernoulli`]: distributions::Bernoulli |
| //! [`Beta`]: distributions::Beta |
| //! [`Binomial`]: distributions::Binomial |
| //! [`Cauchy`]: distributions::Cauchy |
| //! [`ChiSquared`]: distributions::ChiSquared |
| //! [`Dirichlet`]: distributions::Dirichlet |
| //! [`Exp`]: distributions::Exp |
| //! [`Exp1`]: distributions::Exp1 |
| //! [`FisherF`]: distributions::FisherF |
| //! [`Gamma`]: distributions::Gamma |
| //! [`LogNormal`]: distributions::LogNormal |
| //! [`Normal`]: distributions::Normal |
| //! [`Open01`]: distributions::Open01 |
| //! [`OpenClosed01`]: distributions::OpenClosed01 |
| //! [`Pareto`]: distributions::Pareto |
| //! [`Poisson`]: distributions::Poisson |
| //! [`Standard`]: distributions::Standard |
| //! [`StandardNormal`]: distributions::StandardNormal |
| //! [`StudentT`]: distributions::StudentT |
| //! [`Triangular`]: distributions::Triangular |
| //! [`Uniform`]: distributions::Uniform |
| //! [`Uniform::new`]: distributions::Uniform::new |
| //! [`Uniform::new_inclusive`]: distributions::Uniform::new_inclusive |
| //! [`UnitSphereSurface`]: distributions::UnitSphereSurface |
| //! [`UnitCircle`]: distributions::UnitCircle |
| //! [`Weibull`]: distributions::Weibull |
| //! [`WeightedIndex`]: distributions::WeightedIndex |
| |
| #[cfg(any(rustc_1_26, features="nightly"))] |
| use core::iter; |
| use Rng; |
| |
| pub use self::other::Alphanumeric; |
| #[doc(inline)] pub use self::uniform::Uniform; |
| pub use self::float::{OpenClosed01, Open01}; |
| pub use self::bernoulli::Bernoulli; |
| #[cfg(feature="alloc")] pub use self::weighted::{WeightedIndex, WeightedError}; |
| #[cfg(feature="std")] pub use self::unit_sphere::UnitSphereSurface; |
| #[cfg(feature="std")] pub use self::unit_circle::UnitCircle; |
| #[cfg(feature="std")] pub use self::gamma::{Gamma, ChiSquared, FisherF, |
| StudentT, Beta}; |
| #[cfg(feature="std")] pub use self::normal::{Normal, LogNormal, StandardNormal}; |
| #[cfg(feature="std")] pub use self::exponential::{Exp, Exp1}; |
| #[cfg(feature="std")] pub use self::pareto::Pareto; |
| #[cfg(feature="std")] pub use self::poisson::Poisson; |
| #[cfg(feature="std")] pub use self::binomial::Binomial; |
| #[cfg(feature="std")] pub use self::cauchy::Cauchy; |
| #[cfg(feature="std")] pub use self::dirichlet::Dirichlet; |
| #[cfg(feature="std")] pub use self::triangular::Triangular; |
| #[cfg(feature="std")] pub use self::weibull::Weibull; |
| |
| pub mod uniform; |
| mod bernoulli; |
| #[cfg(feature="alloc")] mod weighted; |
| #[cfg(feature="std")] mod unit_sphere; |
| #[cfg(feature="std")] mod unit_circle; |
| #[cfg(feature="std")] mod gamma; |
| #[cfg(feature="std")] mod normal; |
| #[cfg(feature="std")] mod exponential; |
| #[cfg(feature="std")] mod pareto; |
| #[cfg(feature="std")] mod poisson; |
| #[cfg(feature="std")] mod binomial; |
| #[cfg(feature="std")] mod cauchy; |
| #[cfg(feature="std")] mod dirichlet; |
| #[cfg(feature="std")] mod triangular; |
| #[cfg(feature="std")] mod weibull; |
| |
| mod float; |
| mod integer; |
| mod other; |
| mod utils; |
| #[cfg(feature="std")] mod ziggurat_tables; |
| |
| /// Types (distributions) that can be used to create a random instance of `T`. |
| /// |
| /// It is possible to sample from a distribution through both the |
| /// `Distribution` and [`Rng`] traits, via `distr.sample(&mut rng)` and |
| /// `rng.sample(distr)`. They also both offer the [`sample_iter`] method, which |
| /// produces an iterator that samples from the distribution. |
| /// |
| /// All implementations are expected to be immutable; this has the significant |
| /// advantage of not needing to consider thread safety, and for most |
| /// distributions efficient state-less sampling algorithms are available. |
| /// |
| /// [`sample_iter`]: Distribution::method.sample_iter |
| pub trait Distribution<T> { |
| /// Generate a random value of `T`, using `rng` as the source of randomness. |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T; |
| |
| /// Create an iterator that generates random values of `T`, using `rng` as |
| /// the source of randomness. |
| /// |
| /// # Example |
| /// |
| /// ``` |
| /// use rand::thread_rng; |
| /// use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard}; |
| /// |
| /// let mut rng = thread_rng(); |
| /// |
| /// // Vec of 16 x f32: |
| /// let v: Vec<f32> = Standard.sample_iter(&mut rng).take(16).collect(); |
| /// |
| /// // String: |
| /// let s: String = Alphanumeric.sample_iter(&mut rng).take(7).collect(); |
| /// |
| /// // Dice-rolling: |
| /// let die_range = Uniform::new_inclusive(1, 6); |
| /// let mut roll_die = die_range.sample_iter(&mut rng); |
| /// while roll_die.next().unwrap() != 6 { |
| /// println!("Not a 6; rolling again!"); |
| /// } |
| /// ``` |
| fn sample_iter<'a, R>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T> |
| where Self: Sized, R: Rng |
| { |
| DistIter { |
| distr: self, |
| rng: rng, |
| phantom: ::core::marker::PhantomData, |
| } |
| } |
| } |
| |
| impl<'a, T, D: Distribution<T>> Distribution<T> for &'a D { |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T { |
| (*self).sample(rng) |
| } |
| } |
| |
| |
| /// An iterator that generates random values of `T` with distribution `D`, |
| /// using `R` as the source of randomness. |
| /// |
| /// This `struct` is created by the [`sample_iter`] method on [`Distribution`]. |
| /// See its documentation for more. |
| /// |
| /// [`sample_iter`]: Distribution::sample_iter |
| #[derive(Debug)] |
| pub struct DistIter<'a, D: 'a, R: 'a, T> { |
| distr: &'a D, |
| rng: &'a mut R, |
| phantom: ::core::marker::PhantomData<T>, |
| } |
| |
| impl<'a, D, R, T> Iterator for DistIter<'a, D, R, T> |
| where D: Distribution<T>, R: Rng + 'a |
| { |
| type Item = T; |
| |
| #[inline(always)] |
| fn next(&mut self) -> Option<T> { |
| Some(self.distr.sample(self.rng)) |
| } |
| |
| fn size_hint(&self) -> (usize, Option<usize>) { |
| (usize::max_value(), None) |
| } |
| } |
| |
| #[cfg(rustc_1_26)] |
| impl<'a, D, R, T> iter::FusedIterator for DistIter<'a, D, R, T> |
| where D: Distribution<T>, R: Rng + 'a {} |
| |
| #[cfg(features = "nightly")] |
| impl<'a, D, R, T> iter::TrustedLen for DistIter<'a, D, R, T> |
| where D: Distribution<T>, R: Rng + 'a {} |
| |
| |
| /// 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. |
| /// |
| /// ## Built-in 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 following aggregate types also implement the distribution `Standard` as |
| /// long as their component types implement it: |
| /// |
| /// * Tuples and arrays: Each element of the tuple or array is generated |
| /// independently, using the `Standard` distribution recursively. |
| /// * `Option<T>` where `Standard` is implemented for `T`: Returns `None` with |
| /// probability 0.5; otherwise generates a random `x: T` and returns `Some(x)`. |
| /// |
| /// # Example |
| /// ``` |
| /// use rand::prelude::*; |
| /// use rand::distributions::Standard; |
| /// |
| /// let val: f32 = SmallRng::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 23 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` and `gen_range` (which uses [`Uniform`]) use |
| /// 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)] |
| pub struct Standard; |
| |
| |
| /// A value with a particular weight for use with `WeightedChoice`. |
| #[deprecated(since="0.6.0", note="use WeightedIndex instead")] |
| #[allow(deprecated)] |
| #[derive(Copy, Clone, Debug)] |
| pub struct Weighted<T> { |
| /// The numerical weight of this item |
| pub weight: u32, |
| /// The actual item which is being weighted |
| pub item: T, |
| } |
| |
| /// A distribution that selects from a finite collection of weighted items. |
| /// |
| /// Deprecated: use [`WeightedIndex`] instead. |
| /// |
| /// [`WeightedIndex`]: WeightedIndex |
| #[deprecated(since="0.6.0", note="use WeightedIndex instead")] |
| #[allow(deprecated)] |
| #[derive(Debug)] |
| pub struct WeightedChoice<'a, T:'a> { |
| items: &'a mut [Weighted<T>], |
| weight_range: Uniform<u32>, |
| } |
| |
| #[deprecated(since="0.6.0", note="use WeightedIndex instead")] |
| #[allow(deprecated)] |
| impl<'a, T: Clone> WeightedChoice<'a, T> { |
| /// Create a new `WeightedChoice`. |
| /// |
| /// Panics if: |
| /// |
| /// - `items` is empty |
| /// - the total weight is 0 |
| /// - the total weight is larger than a `u32` can contain. |
| pub fn new(items: &'a mut [Weighted<T>]) -> WeightedChoice<'a, T> { |
| // strictly speaking, this is subsumed by the total weight == 0 case |
| assert!(!items.is_empty(), "WeightedChoice::new called with no items"); |
| |
| let mut running_total: u32 = 0; |
| |
| // we convert the list from individual weights to cumulative |
| // weights so we can binary search. This *could* drop elements |
| // with weight == 0 as an optimisation. |
| for item in items.iter_mut() { |
| running_total = match running_total.checked_add(item.weight) { |
| Some(n) => n, |
| None => panic!("WeightedChoice::new called with a total weight \ |
| larger than a u32 can contain") |
| }; |
| |
| item.weight = running_total; |
| } |
| assert!(running_total != 0, "WeightedChoice::new called with a total weight of 0"); |
| |
| WeightedChoice { |
| items, |
| // we're likely to be generating numbers in this range |
| // relatively often, so might as well cache it |
| weight_range: Uniform::new(0, running_total) |
| } |
| } |
| } |
| |
| #[deprecated(since="0.6.0", note="use WeightedIndex instead")] |
| #[allow(deprecated)] |
| impl<'a, T: Clone> Distribution<T> for WeightedChoice<'a, T> { |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T { |
| // we want to find the first element that has cumulative |
| // weight > sample_weight, which we do by binary since the |
| // cumulative weights of self.items are sorted. |
| |
| // choose a weight in [0, total_weight) |
| let sample_weight = self.weight_range.sample(rng); |
| |
| // short circuit when it's the first item |
| if sample_weight < self.items[0].weight { |
| return self.items[0].item.clone(); |
| } |
| |
| let mut idx = 0; |
| let mut modifier = self.items.len(); |
| |
| // now we know that every possibility has an element to the |
| // left, so we can just search for the last element that has |
| // cumulative weight <= sample_weight, then the next one will |
| // be "it". (Note that this greatest element will never be the |
| // last element of the vector, since sample_weight is chosen |
| // in [0, total_weight) and the cumulative weight of the last |
| // one is exactly the total weight.) |
| while modifier > 1 { |
| let i = idx + modifier / 2; |
| if self.items[i].weight <= sample_weight { |
| // we're small, so look to the right, but allow this |
| // exact element still. |
| idx = i; |
| // we need the `/ 2` to round up otherwise we'll drop |
| // the trailing elements when `modifier` is odd. |
| modifier += 1; |
| } else { |
| // otherwise we're too big, so go left. (i.e. do |
| // nothing) |
| } |
| modifier /= 2; |
| } |
| self.items[idx + 1].item.clone() |
| } |
| } |
| |
| #[cfg(test)] |
| mod tests { |
| use rngs::mock::StepRng; |
| #[allow(deprecated)] |
| use super::{WeightedChoice, Weighted, Distribution}; |
| |
| #[test] |
| #[allow(deprecated)] |
| fn test_weighted_choice() { |
| // this makes assumptions about the internal implementation of |
| // WeightedChoice. It may fail when the implementation in |
| // `distributions::uniform::UniformInt` changes. |
| |
| macro_rules! t { |
| ($items:expr, $expected:expr) => {{ |
| let mut items = $items; |
| let mut total_weight = 0; |
| for item in &items { total_weight += item.weight; } |
| |
| let wc = WeightedChoice::new(&mut items); |
| let expected = $expected; |
| |
| // Use extremely large steps between the random numbers, because |
| // we test with small ranges and `UniformInt` is designed to prefer |
| // the most significant bits. |
| let mut rng = StepRng::new(0, !0 / (total_weight as u64)); |
| |
| for &val in expected.iter() { |
| assert_eq!(wc.sample(&mut rng), val) |
| } |
| }} |
| } |
| |
| t!([Weighted { weight: 1, item: 10}], [10]); |
| |
| // skip some |
| t!([Weighted { weight: 0, item: 20}, |
| Weighted { weight: 2, item: 21}, |
| Weighted { weight: 0, item: 22}, |
| Weighted { weight: 1, item: 23}], |
| [21, 21, 23]); |
| |
| // different weights |
| t!([Weighted { weight: 4, item: 30}, |
| Weighted { weight: 3, item: 31}], |
| [30, 31, 30, 31, 30, 31, 30]); |
| |
| // check that we're binary searching |
| // correctly with some vectors of odd |
| // length. |
| t!([Weighted { weight: 1, item: 40}, |
| Weighted { weight: 1, item: 41}, |
| Weighted { weight: 1, item: 42}, |
| Weighted { weight: 1, item: 43}, |
| Weighted { weight: 1, item: 44}], |
| [40, 41, 42, 43, 44]); |
| t!([Weighted { weight: 1, item: 50}, |
| Weighted { weight: 1, item: 51}, |
| Weighted { weight: 1, item: 52}, |
| Weighted { weight: 1, item: 53}, |
| Weighted { weight: 1, item: 54}, |
| Weighted { weight: 1, item: 55}, |
| Weighted { weight: 1, item: 56}], |
| [50, 54, 51, 55, 52, 56, 53]); |
| } |
| |
| #[test] |
| #[allow(deprecated)] |
| fn test_weighted_clone_initialization() { |
| let initial : Weighted<u32> = Weighted {weight: 1, item: 1}; |
| let clone = initial.clone(); |
| assert_eq!(initial.weight, clone.weight); |
| assert_eq!(initial.item, clone.item); |
| } |
| |
| #[test] #[should_panic] |
| #[allow(deprecated)] |
| fn test_weighted_clone_change_weight() { |
| let initial : Weighted<u32> = Weighted {weight: 1, item: 1}; |
| let mut clone = initial.clone(); |
| clone.weight = 5; |
| assert_eq!(initial.weight, clone.weight); |
| } |
| |
| #[test] #[should_panic] |
| #[allow(deprecated)] |
| fn test_weighted_clone_change_item() { |
| let initial : Weighted<u32> = Weighted {weight: 1, item: 1}; |
| let mut clone = initial.clone(); |
| clone.item = 5; |
| assert_eq!(initial.item, clone.item); |
| |
| } |
| |
| #[test] #[should_panic] |
| #[allow(deprecated)] |
| fn test_weighted_choice_no_items() { |
| WeightedChoice::<isize>::new(&mut []); |
| } |
| #[test] #[should_panic] |
| #[allow(deprecated)] |
| fn test_weighted_choice_zero_weight() { |
| WeightedChoice::new(&mut [Weighted { weight: 0, item: 0}, |
| Weighted { weight: 0, item: 1}]); |
| } |
| #[test] #[should_panic] |
| #[allow(deprecated)] |
| fn test_weighted_choice_weight_overflows() { |
| let x = ::core::u32::MAX / 2; // x + x + 2 is the overflow |
| WeightedChoice::new(&mut [Weighted { weight: x, item: 0 }, |
| Weighted { weight: 1, item: 1 }, |
| Weighted { weight: x, item: 2 }, |
| Weighted { weight: 1, item: 3 }]); |
| } |
| |
| #[cfg(feature="std")] |
| #[test] |
| fn test_distributions_iter() { |
| use distributions::Normal; |
| let mut rng = ::test::rng(210); |
| let distr = Normal::new(10.0, 10.0); |
| let results: Vec<_> = distr.sample_iter(&mut rng).take(100).collect(); |
| println!("{:?}", results); |
| } |
| } |