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// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// 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).
#[cfg(feature = "alloc")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
pub mod index;
#[cfg(feature = "alloc")] use core::ops::Index;
#[cfg(feature = "alloc")] use alloc::vec::Vec;
#[cfg(feature = "alloc")]
use crate::distributions::uniform::{SampleBorrow, SampleUniform};
#[cfg(feature = "alloc")] use crate::distributions::WeightedError;
use crate::Rng;
/// Extension trait on slices, providing random mutation and sampling methods.
///
/// This trait is implemented on all `[T]` slice types, providing several
/// methods for choosing and shuffling elements. You must `use` this trait:
///
/// ```
/// use rand::seq::SliceRandom;
///
/// let mut rng = rand::thread_rng();
/// let mut bytes = "Hello, random!".to_string().into_bytes();
/// bytes.shuffle(&mut rng);
/// let str = String::from_utf8(bytes).unwrap();
/// println!("{}", str);
/// ```
/// Example output (non-deterministic):
/// ```none
/// l,nmroHado !le
/// ```
pub trait SliceRandom {
/// The element type.
type Item;
/// Returns a reference to one random element of the slice, or `None` if the
/// slice is empty.
///
/// For slices, complexity is `O(1)`.
///
/// # Example
///
/// ```
/// use rand::thread_rng;
/// use rand::seq::SliceRandom;
///
/// let choices = [1, 2, 4, 8, 16, 32];
/// let mut rng = thread_rng();
/// println!("{:?}", choices.choose(&mut rng));
/// assert_eq!(choices[..0].choose(&mut rng), None);
/// ```
fn choose<R>(&self, rng: &mut R) -> Option<&Self::Item>
where R: Rng + ?Sized;
/// Returns a mutable reference to one random element of the slice, or
/// `None` if the slice is empty.
///
/// For slices, complexity is `O(1)`.
fn choose_mut<R>(&mut self, rng: &mut R) -> Option<&mut Self::Item>
where R: Rng + ?Sized;
/// Chooses `amount` elements from the slice at random, without repetition,
/// and in random order. The returned iterator is appropriate both for
/// collection into a `Vec` and filling an existing buffer (see example).
///
/// In case this API is not sufficiently flexible, use [`index::sample`].
///
/// For slices, complexity is the same as [`index::sample`].
///
/// # Example
/// ```
/// use rand::seq::SliceRandom;
///
/// let mut rng = &mut rand::thread_rng();
/// let sample = "Hello, audience!".as_bytes();
///
/// // collect the results into a vector:
/// let v: Vec<u8> = sample.choose_multiple(&mut rng, 3).cloned().collect();
///
/// // store in a buffer:
/// let mut buf = [0u8; 5];
/// for (b, slot) in sample.choose_multiple(&mut rng, buf.len()).zip(buf.iter_mut()) {
/// *slot = *b;
/// }
/// ```
#[cfg(feature = "alloc")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
fn choose_multiple<R>(&self, rng: &mut R, amount: usize) -> SliceChooseIter<Self, Self::Item>
where R: Rng + ?Sized;
/// Similar to [`choose`], but where the likelihood of each outcome may be
/// specified.
///
/// The specified function `weight` maps each item `x` to a relative
/// likelihood `weight(x)`. The probability of each item being selected is
/// therefore `weight(x) / s`, where `s` is the sum of all `weight(x)`.
///
/// For slices of length `n`, complexity is `O(n)`.
/// See also [`choose_weighted_mut`], [`distributions::weighted`].
///
/// # Example
///
/// ```
/// use rand::prelude::*;
///
/// let choices = [('a', 2), ('b', 1), ('c', 1)];
/// let mut rng = thread_rng();
/// // 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
/// println!("{:?}", choices.choose_weighted(&mut rng, |item| item.1).unwrap().0);
/// ```
/// [`choose`]: SliceRandom::choose
/// [`choose_weighted_mut`]: SliceRandom::choose_weighted_mut
/// [`distributions::weighted`]: crate::distributions::weighted
#[cfg(feature = "alloc")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
fn choose_weighted<R, F, B, X>(
&self, rng: &mut R, weight: F,
) -> Result<&Self::Item, WeightedError>
where
R: Rng + ?Sized,
F: Fn(&Self::Item) -> B,
B: SampleBorrow<X>,
X: SampleUniform
+ for<'a> ::core::ops::AddAssign<&'a X>
+ ::core::cmp::PartialOrd<X>
+ Clone
+ Default;
/// Similar to [`choose_mut`], but where the likelihood of each outcome may
/// be specified.
///
/// The specified function `weight` maps each item `x` to a relative
/// likelihood `weight(x)`. The probability of each item being selected is
/// therefore `weight(x) / s`, where `s` is the sum of all `weight(x)`.
///
/// For slices of length `n`, complexity is `O(n)`.
/// See also [`choose_weighted`], [`distributions::weighted`].
///
/// [`choose_mut`]: SliceRandom::choose_mut
/// [`choose_weighted`]: SliceRandom::choose_weighted
/// [`distributions::weighted`]: crate::distributions::weighted
#[cfg(feature = "alloc")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
fn choose_weighted_mut<R, F, B, X>(
&mut self, rng: &mut R, weight: F,
) -> Result<&mut Self::Item, WeightedError>
where
R: Rng + ?Sized,
F: Fn(&Self::Item) -> B,
B: SampleBorrow<X>,
X: SampleUniform
+ for<'a> ::core::ops::AddAssign<&'a X>
+ ::core::cmp::PartialOrd<X>
+ Clone
+ Default;
/// Similar to [`choose_multiple`], but where the likelihood of each element's
/// inclusion in the output may be specified. The elements are returned in an
/// arbitrary, unspecified order.
///
/// The specified function `weight` maps each item `x` to a relative
/// likelihood `weight(x)`. The probability of each item being selected is
/// therefore `weight(x) / s`, where `s` is the sum of all `weight(x)`.
///
/// If all of the weights are equal, even if they are all zero, each element has
/// an equal likelihood of being selected.
///
/// The complexity of this method depends on the feature `partition_at_index`.
/// If the feature is enabled, then for slices of length `n`, the complexity
/// is `O(n)` space and `O(n)` time. Otherwise, the complexity is `O(n)` space and
/// `O(n * log amount)` time.
///
/// # Example
///
/// ```
/// use rand::prelude::*;
///
/// let choices = [('a', 2), ('b', 1), ('c', 1)];
/// let mut rng = thread_rng();
/// // First Draw * Second Draw = total odds
/// // -----------------------
/// // (50% * 50%) + (25% * 67%) = 41.7% chance that the output is `['a', 'b']` in some order.
/// // (50% * 50%) + (25% * 67%) = 41.7% chance that the output is `['a', 'c']` in some order.
/// // (25% * 33%) + (25% * 33%) = 16.6% chance that the output is `['b', 'c']` in some order.
/// println!("{:?}", choices.choose_multiple_weighted(&mut rng, 2, |item| item.1).unwrap().collect::<Vec<_>>());
/// ```
/// [`choose_multiple`]: SliceRandom::choose_multiple
//
// Note: this is feature-gated on std due to usage of f64::powf.
// If necessary, we may use alloc+libm as an alternative (see PR #1089).
#[cfg(feature = "std")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "std")))]
fn choose_multiple_weighted<R, F, X>(
&self, rng: &mut R, amount: usize, weight: F,
) -> Result<SliceChooseIter<Self, Self::Item>, WeightedError>
where
R: Rng + ?Sized,
F: Fn(&Self::Item) -> X,
X: Into<f64>;
/// Shuffle a mutable slice in place.
///
/// For slices of length `n`, complexity is `O(n)`.
///
/// # Example
///
/// ```
/// use rand::seq::SliceRandom;
/// use rand::thread_rng;
///
/// let mut rng = thread_rng();
/// let mut y = [1, 2, 3, 4, 5];
/// println!("Unshuffled: {:?}", y);
/// y.shuffle(&mut rng);
/// println!("Shuffled: {:?}", y);
/// ```
fn shuffle<R>(&mut self, rng: &mut R)
where R: Rng + ?Sized;
/// Shuffle a slice in place, but exit early.
///
/// Returns two mutable slices from the source slice. The first contains
/// `amount` elements randomly permuted. The second has the remaining
/// elements that are not fully shuffled.
///
/// This is an efficient method to select `amount` elements at random from
/// the slice, provided the slice may be mutated.
///
/// If you only need to choose elements randomly and `amount > self.len()/2`
/// then you may improve performance by taking
/// `amount = values.len() - amount` and using only the second slice.
///
/// If `amount` is greater than the number of elements in the slice, this
/// will perform a full shuffle.
///
/// For slices, complexity is `O(m)` where `m = amount`.
fn partial_shuffle<R>(
&mut self, rng: &mut R, amount: usize,
) -> (&mut [Self::Item], &mut [Self::Item])
where R: Rng + ?Sized;
}
/// Extension trait on iterators, providing random sampling methods.
///
/// This trait is implemented on all iterators `I` where `I: Iterator + Sized`
/// and provides methods for
/// choosing one or more elements. You must `use` this trait:
///
/// ```
/// use rand::seq::IteratorRandom;
///
/// let mut rng = rand::thread_rng();
///
/// let faces = "😀😎😐😕😠😢";
/// println!("I am {}!", faces.chars().choose(&mut rng).unwrap());
/// ```
/// Example output (non-deterministic):
/// ```none
/// I am 😀!
/// ```
pub trait IteratorRandom: Iterator + Sized {
/// Choose one element at random from the iterator.
///
/// Returns `None` if and only if the iterator is empty.
///
/// This method uses [`Iterator::size_hint`] for optimisation. With an
/// accurate hint and where [`Iterator::nth`] is a constant-time operation
/// this method can offer `O(1)` performance. Where no size hint is
/// available, complexity is `O(n)` where `n` is the iterator length.
/// Partial hints (where `lower > 0`) also improve performance.
///
/// Note that the output values and the number of RNG samples used
/// depends on size hints. In particular, `Iterator` combinators that don't
/// change the values yielded but change the size hints may result in
/// `choose` returning different elements. If you want consistent results
/// and RNG usage consider using [`IteratorRandom::choose_stable`].
fn choose<R>(mut self, rng: &mut R) -> Option<Self::Item>
where R: Rng + ?Sized {
let (mut lower, mut upper) = self.size_hint();
let mut consumed = 0;
let mut result = None;
// Handling for this condition outside the loop allows the optimizer to eliminate the loop
// when the Iterator is an ExactSizeIterator. This has a large performance impact on e.g.
// seq_iter_choose_from_1000.
if upper == Some(lower) {
return if lower == 0 {
None
} else {
self.nth(gen_index(rng, lower))
};
}
// Continue until the iterator is exhausted
loop {
if lower > 1 {
let ix = gen_index(rng, lower + consumed);
let skip = if ix < lower {
result = self.nth(ix);
lower - (ix + 1)
} else {
lower
};
if upper == Some(lower) {
return result;
}
consumed += lower;
if skip > 0 {
self.nth(skip - 1);
}
} else {
let elem = self.next();
if elem.is_none() {
return result;
}
consumed += 1;
if gen_index(rng, consumed) == 0 {
result = elem;
}
}
let hint = self.size_hint();
lower = hint.0;
upper = hint.1;
}
}
/// Choose one element at random from the iterator.
///
/// Returns `None` if and only if the iterator is empty.
///
/// This method is very similar to [`choose`] except that the result
/// only depends on the length of the iterator and the values produced by
/// `rng`. Notably for any iterator of a given length this will make the
/// same requests to `rng` and if the same sequence of values are produced
/// the same index will be selected from `self`. This may be useful if you
/// need consistent results no matter what type of iterator you are working
/// with. If you do not need this stability prefer [`choose`].
///
/// Note that this method still uses [`Iterator::size_hint`] to skip
/// constructing elements where possible, however the selection and `rng`
/// calls are the same in the face of this optimization. If you want to
/// force every element to be created regardless call `.inspect(|e| ())`.
///
/// [`choose`]: IteratorRandom::choose
fn choose_stable<R>(mut self, rng: &mut R) -> Option<Self::Item>
where R: Rng + ?Sized {
let mut consumed = 0;
let mut result = None;
loop {
// Currently the only way to skip elements is `nth()`. So we need to
// store what index to access next here.
// This should be replaced by `advance_by()` once it is stable:
let mut next = 0;
let (lower, _) = self.size_hint();
if lower >= 2 {
let highest_selected = (0..lower)
.filter(|ix| gen_index(rng, consumed+ix+1) == 0)
.last();
consumed += lower;
next = lower;
if let Some(ix) = highest_selected {
result = self.nth(ix);
next -= ix + 1;
debug_assert!(result.is_some(), "iterator shorter than size_hint().0");
}
}
let elem = self.nth(next);
if elem.is_none() {
return result
}
if gen_index(rng, consumed+1) == 0 {
result = elem;
}
consumed += 1;
}
}
/// Collects values at random from the iterator into a supplied buffer
/// until that buffer is filled.
///
/// Although the elements are selected randomly, the order of elements in
/// the buffer is neither stable nor fully random. If random ordering is
/// desired, shuffle the result.
///
/// Returns the number of elements added to the buffer. This equals the length
/// of the buffer unless the iterator contains insufficient elements, in which
/// case this equals the number of elements available.
///
/// Complexity is `O(n)` where `n` is the length of the iterator.
/// For slices, prefer [`SliceRandom::choose_multiple`].
fn choose_multiple_fill<R>(mut self, rng: &mut R, buf: &mut [Self::Item]) -> usize
where R: Rng + ?Sized {
let amount = buf.len();
let mut len = 0;
while len < amount {
if let Some(elem) = self.next() {
buf[len] = elem;
len += 1;
} else {
// Iterator exhausted; stop early
return len;
}
}
// Continue, since the iterator was not exhausted
for (i, elem) in self.enumerate() {
let k = gen_index(rng, i + 1 + amount);
if let Some(slot) = buf.get_mut(k) {
*slot = elem;
}
}
len
}
/// Collects `amount` values at random from the iterator into a vector.
///
/// This is equivalent to `choose_multiple_fill` except for the result type.
///
/// Although the elements are selected randomly, the order of elements in
/// the buffer is neither stable nor fully random. If random ordering is
/// desired, shuffle the result.
///
/// The length of the returned vector equals `amount` unless the iterator
/// contains insufficient elements, in which case it equals the number of
/// elements available.
///
/// Complexity is `O(n)` where `n` is the length of the iterator.
/// For slices, prefer [`SliceRandom::choose_multiple`].
#[cfg(feature = "alloc")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
fn choose_multiple<R>(mut self, rng: &mut R, amount: usize) -> Vec<Self::Item>
where R: Rng + ?Sized {
let mut reservoir = Vec::with_capacity(amount);
reservoir.extend(self.by_ref().take(amount));
// Continue unless the iterator was exhausted
//
// note: this prevents iterators that "restart" from causing problems.
// If the iterator stops once, then so do we.
if reservoir.len() == amount {
for (i, elem) in self.enumerate() {
let k = gen_index(rng, i + 1 + amount);
if let Some(slot) = reservoir.get_mut(k) {
*slot = elem;
}
}
} else {
// Don't hang onto extra memory. There is a corner case where
// `amount` was much less than `self.len()`.
reservoir.shrink_to_fit();
}
reservoir
}
}
impl<T> SliceRandom for [T] {
type Item = T;
fn choose<R>(&self, rng: &mut R) -> Option<&Self::Item>
where R: Rng + ?Sized {
if self.is_empty() {
None
} else {
Some(&self[gen_index(rng, self.len())])
}
}
fn choose_mut<R>(&mut self, rng: &mut R) -> Option<&mut Self::Item>
where R: Rng + ?Sized {
if self.is_empty() {
None
} else {
let len = self.len();
Some(&mut self[gen_index(rng, len)])
}
}
#[cfg(feature = "alloc")]
fn choose_multiple<R>(&self, rng: &mut R, amount: usize) -> SliceChooseIter<Self, Self::Item>
where R: Rng + ?Sized {
let amount = ::core::cmp::min(amount, self.len());
SliceChooseIter {
slice: self,
_phantom: Default::default(),
indices: index::sample(rng, self.len(), amount).into_iter(),
}
}
#[cfg(feature = "alloc")]
fn choose_weighted<R, F, B, X>(
&self, rng: &mut R, weight: F,
) -> Result<&Self::Item, WeightedError>
where
R: Rng + ?Sized,
F: Fn(&Self::Item) -> B,
B: SampleBorrow<X>,
X: SampleUniform
+ for<'a> ::core::ops::AddAssign<&'a X>
+ ::core::cmp::PartialOrd<X>
+ Clone
+ Default,
{
use crate::distributions::{Distribution, WeightedIndex};
let distr = WeightedIndex::new(self.iter().map(weight))?;
Ok(&self[distr.sample(rng)])
}
#[cfg(feature = "alloc")]
fn choose_weighted_mut<R, F, B, X>(
&mut self, rng: &mut R, weight: F,
) -> Result<&mut Self::Item, WeightedError>
where
R: Rng + ?Sized,
F: Fn(&Self::Item) -> B,
B: SampleBorrow<X>,
X: SampleUniform
+ for<'a> ::core::ops::AddAssign<&'a X>
+ ::core::cmp::PartialOrd<X>
+ Clone
+ Default,
{
use crate::distributions::{Distribution, WeightedIndex};
let distr = WeightedIndex::new(self.iter().map(weight))?;
Ok(&mut self[distr.sample(rng)])
}
#[cfg(feature = "std")]
fn choose_multiple_weighted<R, F, X>(
&self, rng: &mut R, amount: usize, weight: F,
) -> Result<SliceChooseIter<Self, Self::Item>, WeightedError>
where
R: Rng + ?Sized,
F: Fn(&Self::Item) -> X,
X: Into<f64>,
{
let amount = ::core::cmp::min(amount, self.len());
Ok(SliceChooseIter {
slice: self,
_phantom: Default::default(),
indices: index::sample_weighted(
rng,
self.len(),
|idx| weight(&self[idx]).into(),
amount,
)?
.into_iter(),
})
}
fn shuffle<R>(&mut self, rng: &mut R)
where R: Rng + ?Sized {
for i in (1..self.len()).rev() {
// invariant: elements with index > i have been locked in place.
self.swap(i, gen_index(rng, i + 1));
}
}
fn partial_shuffle<R>(
&mut self, rng: &mut R, amount: usize,
) -> (&mut [Self::Item], &mut [Self::Item])
where R: Rng + ?Sized {
// This applies Durstenfeld's algorithm for the
// [Fisher–Yates shuffle](https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm)
// for an unbiased permutation, but exits early after choosing `amount`
// elements.
let len = self.len();
let end = if amount >= len { 0 } else { len - amount };
for i in (end..len).rev() {
// invariant: elements with index > i have been locked in place.
self.swap(i, gen_index(rng, i + 1));
}
let r = self.split_at_mut(end);
(r.1, r.0)
}
}
impl<I> IteratorRandom for I where I: Iterator + Sized {}
/// An iterator over multiple slice elements.
///
/// This struct is created by
/// [`SliceRandom::choose_multiple`](trait.SliceRandom.html#tymethod.choose_multiple).
#[cfg(feature = "alloc")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
#[derive(Debug)]
pub struct SliceChooseIter<'a, S: ?Sized + 'a, T: 'a> {
slice: &'a S,
_phantom: ::core::marker::PhantomData<T>,
indices: index::IndexVecIntoIter,
}
#[cfg(feature = "alloc")]
impl<'a, S: Index<usize, Output = T> + ?Sized + 'a, T: 'a> Iterator for SliceChooseIter<'a, S, T> {
type Item = &'a T;
fn next(&mut self) -> Option<Self::Item> {
// TODO: investigate using SliceIndex::get_unchecked when stable
self.indices.next().map(|i| &self.slice[i as usize])
}
fn size_hint(&self) -> (usize, Option<usize>) {
(self.indices.len(), Some(self.indices.len()))
}
}
#[cfg(feature = "alloc")]
impl<'a, S: Index<usize, Output = T> + ?Sized + 'a, T: 'a> ExactSizeIterator
for SliceChooseIter<'a, S, T>
{
fn len(&self) -> usize {
self.indices.len()
}
}
// Sample a number uniformly between 0 and `ubound`. Uses 32-bit sampling where
// possible, primarily in order to produce the same output on 32-bit and 64-bit
// platforms.
#[inline]
fn gen_index<R: Rng + ?Sized>(rng: &mut R, ubound: usize) -> usize {
if ubound <= (core::u32::MAX as usize) {
rng.gen_range(0..ubound as u32) as usize
} else {
rng.gen_range(0..ubound)
}
}
#[cfg(test)]
mod test {
use super::*;
#[cfg(feature = "alloc")] use crate::Rng;
#[cfg(all(feature = "alloc", not(feature = "std")))] use alloc::vec::Vec;
#[test]
fn test_slice_choose() {
let mut r = crate::test::rng(107);
let chars = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
];
let mut chosen = [0i32; 14];
// The below all use a binomial distribution with n=1000, p=1/14.
// binocdf(40, 1000, 1/14) ~= 2e-5; 1-binocdf(106, ..) ~= 2e-5
for _ in 0..1000 {
let picked = *chars.choose(&mut r).unwrap();
chosen[(picked as usize) - ('a' as usize)] += 1;
}
for count in chosen.iter() {
assert!(40 < *count && *count < 106);
}
chosen.iter_mut().for_each(|x| *x = 0);
for _ in 0..1000 {
*chosen.choose_mut(&mut r).unwrap() += 1;
}
for count in chosen.iter() {
assert!(40 < *count && *count < 106);
}
let mut v: [isize; 0] = [];
assert_eq!(v.choose(&mut r), None);
assert_eq!(v.choose_mut(&mut r), None);
}
#[test]
fn value_stability_slice() {
let mut r = crate::test::rng(413);
let chars = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
];
let mut nums = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
assert_eq!(chars.choose(&mut r), Some(&'l'));
assert_eq!(nums.choose_mut(&mut r), Some(&mut 10));
#[cfg(feature = "alloc")]
assert_eq!(
&chars
.choose_multiple(&mut r, 8)
.cloned()
.collect::<Vec<char>>(),
&['d', 'm', 'b', 'n', 'c', 'k', 'h', 'e']
);
#[cfg(feature = "alloc")]
assert_eq!(chars.choose_weighted(&mut r, |_| 1), Ok(&'f'));
#[cfg(feature = "alloc")]
assert_eq!(nums.choose_weighted_mut(&mut r, |_| 1), Ok(&mut 5));
let mut r = crate::test::rng(414);
nums.shuffle(&mut r);
assert_eq!(nums, [9, 5, 3, 10, 7, 12, 8, 11, 6, 4, 0, 2, 1]);
nums = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
let res = nums.partial_shuffle(&mut r, 6);
assert_eq!(res.0, &mut [7, 4, 8, 6, 9, 3]);
assert_eq!(res.1, &mut [0, 1, 2, 12, 11, 5, 10]);
}
#[derive(Clone)]
struct UnhintedIterator<I: Iterator + Clone> {
iter: I,
}
impl<I: Iterator + Clone> Iterator for UnhintedIterator<I> {
type Item = I::Item;
fn next(&mut self) -> Option<Self::Item> {
self.iter.next()
}
}
#[derive(Clone)]
struct ChunkHintedIterator<I: ExactSizeIterator + Iterator + Clone> {
iter: I,
chunk_remaining: usize,
chunk_size: usize,
hint_total_size: bool,
}
impl<I: ExactSizeIterator + Iterator + Clone> Iterator for ChunkHintedIterator<I> {
type Item = I::Item;
fn next(&mut self) -> Option<Self::Item> {
if self.chunk_remaining == 0 {
self.chunk_remaining = ::core::cmp::min(self.chunk_size, self.iter.len());
}
self.chunk_remaining = self.chunk_remaining.saturating_sub(1);
self.iter.next()
}
fn size_hint(&self) -> (usize, Option<usize>) {
(
self.chunk_remaining,
if self.hint_total_size {
Some(self.iter.len())
} else {
None
},
)
}
}
#[derive(Clone)]
struct WindowHintedIterator<I: ExactSizeIterator + Iterator + Clone> {
iter: I,
window_size: usize,
hint_total_size: bool,
}
impl<I: ExactSizeIterator + Iterator + Clone> Iterator for WindowHintedIterator<I> {
type Item = I::Item;
fn next(&mut self) -> Option<Self::Item> {
self.iter.next()
}
fn size_hint(&self) -> (usize, Option<usize>) {
(
::core::cmp::min(self.iter.len(), self.window_size),
if self.hint_total_size {
Some(self.iter.len())
} else {
None
},
)
}
}
#[test]
#[cfg_attr(miri, ignore)] // Miri is too slow
fn test_iterator_choose() {
let r = &mut crate::test::rng(109);
fn test_iter<R: Rng + ?Sized, Iter: Iterator<Item = usize> + Clone>(r: &mut R, iter: Iter) {
let mut chosen = [0i32; 9];
for _ in 0..1000 {
let picked = iter.clone().choose(r).unwrap();
chosen[picked] += 1;
}
for count in chosen.iter() {
// Samples should follow Binomial(1000, 1/9)
// Octave: binopdf(x, 1000, 1/9) gives the prob of *count == x
// Note: have seen 153, which is unlikely but not impossible.
assert!(
72 < *count && *count < 154,
"count not close to 1000/9: {}",
count
);
}
}
test_iter(r, 0..9);
test_iter(r, [0, 1, 2, 3, 4, 5, 6, 7, 8].iter().cloned());
#[cfg(feature = "alloc")]
test_iter(r, (0..9).collect::<Vec<_>>().into_iter());
test_iter(r, UnhintedIterator { iter: 0..9 });
test_iter(r, ChunkHintedIterator {
iter: 0..9,
chunk_size: 4,
chunk_remaining: 4,
hint_total_size: false,
});
test_iter(r, ChunkHintedIterator {
iter: 0..9,
chunk_size: 4,
chunk_remaining: 4,
hint_total_size: true,
});
test_iter(r, WindowHintedIterator {
iter: 0..9,
window_size: 2,
hint_total_size: false,
});
test_iter(r, WindowHintedIterator {
iter: 0..9,
window_size: 2,
hint_total_size: true,
});
assert_eq!((0..0).choose(r), None);
assert_eq!(UnhintedIterator { iter: 0..0 }.choose(r), None);
}
#[test]
#[cfg_attr(miri, ignore)] // Miri is too slow
fn test_iterator_choose_stable() {
let r = &mut crate::test::rng(109);
fn test_iter<R: Rng + ?Sized, Iter: Iterator<Item = usize> + Clone>(r: &mut R, iter: Iter) {
let mut chosen = [0i32; 9];
for _ in 0..1000 {
let picked = iter.clone().choose_stable(r).unwrap();
chosen[picked] += 1;
}
for count in chosen.iter() {
// Samples should follow Binomial(1000, 1/9)
// Octave: binopdf(x, 1000, 1/9) gives the prob of *count == x
// Note: have seen 153, which is unlikely but not impossible.
assert!(
72 < *count && *count < 154,
"count not close to 1000/9: {}",
count
);
}
}
test_iter(r, 0..9);
test_iter(r, [0, 1, 2, 3, 4, 5, 6, 7, 8].iter().cloned());
#[cfg(feature = "alloc")]
test_iter(r, (0..9).collect::<Vec<_>>().into_iter());
test_iter(r, UnhintedIterator { iter: 0..9 });
test_iter(r, ChunkHintedIterator {
iter: 0..9,
chunk_size: 4,
chunk_remaining: 4,
hint_total_size: false,
});
test_iter(r, ChunkHintedIterator {
iter: 0..9,
chunk_size: 4,
chunk_remaining: 4,
hint_total_size: true,
});
test_iter(r, WindowHintedIterator {
iter: 0..9,
window_size: 2,
hint_total_size: false,
});
test_iter(r, WindowHintedIterator {
iter: 0..9,
window_size: 2,
hint_total_size: true,
});
assert_eq!((0..0).choose(r), None);
assert_eq!(UnhintedIterator { iter: 0..0 }.choose(r), None);
}
#[test]
#[cfg_attr(miri, ignore)] // Miri is too slow
fn test_iterator_choose_stable_stability() {
fn test_iter(iter: impl Iterator<Item = usize> + Clone) -> [i32; 9] {
let r = &mut crate::test::rng(109);
let mut chosen = [0i32; 9];
for _ in 0..1000 {
let picked = iter.clone().choose_stable(r).unwrap();
chosen[picked] += 1;
}
chosen
}
let reference = test_iter(0..9);
assert_eq!(test_iter([0, 1, 2, 3, 4, 5, 6, 7, 8].iter().cloned()), reference);
#[cfg(feature = "alloc")]
assert_eq!(test_iter((0..9).collect::<Vec<_>>().into_iter()), reference);
assert_eq!(test_iter(UnhintedIterator { iter: 0..9 }), reference);
assert_eq!(test_iter(ChunkHintedIterator {
iter: 0..9,
chunk_size: 4,
chunk_remaining: 4,
hint_total_size: false,
}), reference);
assert_eq!(test_iter(ChunkHintedIterator {
iter: 0..9,
chunk_size: 4,
chunk_remaining: 4,
hint_total_size: true,
}), reference);
assert_eq!(test_iter(WindowHintedIterator {
iter: 0..9,
window_size: 2,
hint_total_size: false,
}), reference);
assert_eq!(test_iter(WindowHintedIterator {
iter: 0..9,
window_size: 2,
hint_total_size: true,
}), reference);
}
#[test]
#[cfg_attr(miri, ignore)] // Miri is too slow
fn test_shuffle() {
let mut r = crate::test::rng(108);
let empty: &mut [isize] = &mut [];
empty.shuffle(&mut r);
let mut one = [1];
one.shuffle(&mut r);
let b: &[_] = &[1];
assert_eq!(one, b);
let mut two = [1, 2];
two.shuffle(&mut r);
assert!(two == [1, 2] || two == [2, 1]);
fn move_last(slice: &mut [usize], pos: usize) {
// use slice[pos..].rotate_left(1); once we can use that
let last_val = slice[pos];
for i in pos..slice.len() - 1 {
slice[i] = slice[i + 1];
}
*slice.last_mut().unwrap() = last_val;
}
let mut counts = [0i32; 24];
for _ in 0..10000 {
let mut arr: [usize; 4] = [0, 1, 2, 3];
arr.shuffle(&mut r);
let mut permutation = 0usize;
let mut pos_value = counts.len();
for i in 0..4 {
pos_value /= 4 - i;
let pos = arr.iter().position(|&x| x == i).unwrap();
assert!(pos < (4 - i));
permutation += pos * pos_value;
move_last(&mut arr, pos);
assert_eq!(arr[3], i);
}
for (i, &a) in arr.iter().enumerate() {
assert_eq!(a, i);
}
counts[permutation] += 1;
}
for count in counts.iter() {
// Binomial(10000, 1/24) with average 416.667
// Octave: binocdf(n, 10000, 1/24)
// 99.9% chance samples lie within this range:
assert!(352 <= *count && *count <= 483, "count: {}", count);
}
}
#[test]
fn test_partial_shuffle() {
let mut r = crate::test::rng(118);
let mut empty: [u32; 0] = [];
let res = empty.partial_shuffle(&mut r, 10);
assert_eq!((res.0.len(), res.1.len()), (0, 0));
let mut v = [1, 2, 3, 4, 5];
let res = v.partial_shuffle(&mut r, 2);
assert_eq!((res.0.len(), res.1.len()), (2, 3));
assert!(res.0[0] != res.0[1]);
// First elements are only modified if selected, so at least one isn't modified:
assert!(res.1[0] == 1 || res.1[1] == 2 || res.1[2] == 3);
}
#[test]
#[cfg(feature = "alloc")]
fn test_sample_iter() {
let min_val = 1;
let max_val = 100;
let mut r = crate::test::rng(401);
let vals = (min_val..max_val).collect::<Vec<i32>>();
let small_sample = vals.iter().choose_multiple(&mut r, 5);
let large_sample = vals.iter().choose_multiple(&mut r, vals.len() + 5);
assert_eq!(small_sample.len(), 5);
assert_eq!(large_sample.len(), vals.len());
// no randomization happens when amount >= len
assert_eq!(large_sample, vals.iter().collect::<Vec<_>>());
assert!(small_sample
.iter()
.all(|e| { **e >= min_val && **e <= max_val }));
}
#[test]
#[cfg(feature = "alloc")]
#[cfg_attr(miri, ignore)] // Miri is too slow
fn test_weighted() {
let mut r = crate::test::rng(406);
const N_REPS: u32 = 3000;
let weights = [1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7];
let total_weight = weights.iter().sum::<u32>() as f32;
let verify = |result: [i32; 14]| {
for (i, count) in result.iter().enumerate() {
let exp = (weights[i] * N_REPS) as f32 / total_weight;
let mut err = (*count as f32 - exp).abs();
if err != 0.0 {
err /= exp;
}
assert!(err <= 0.25);
}
};
// choose_weighted
fn get_weight<T>(item: &(u32, T)) -> u32 {
item.0
}
let mut chosen = [0i32; 14];
let mut items = [(0u32, 0usize); 14]; // (weight, index)
for (i, item) in items.iter_mut().enumerate() {
*item = (weights[i], i);
}
for _ in 0..N_REPS {
let item = items.choose_weighted(&mut r, get_weight).unwrap();
chosen[item.1] += 1;
}
verify(chosen);
// choose_weighted_mut
let mut items = [(0u32, 0i32); 14]; // (weight, count)
for (i, item) in items.iter_mut().enumerate() {
*item = (weights[i], 0);
}
for _ in 0..N_REPS {
items.choose_weighted_mut(&mut r, get_weight).unwrap().1 += 1;
}
for (ch, item) in chosen.iter_mut().zip(items.iter()) {
*ch = item.1;
}
verify(chosen);
// Check error cases
let empty_slice = &mut [10][0..0];
assert_eq!(
empty_slice.choose_weighted(&mut r, |_| 1),
Err(WeightedError::NoItem)
);
assert_eq!(
empty_slice.choose_weighted_mut(&mut r, |_| 1),
Err(WeightedError::NoItem)
);
assert_eq!(
['x'].choose_weighted_mut(&mut r, |_| 0),
Err(WeightedError::AllWeightsZero)
);
assert_eq!(
[0, -1].choose_weighted_mut(&mut r, |x| *x),
Err(WeightedError::InvalidWeight)
);
assert_eq!(
[-1, 0].choose_weighted_mut(&mut r, |x| *x),
Err(WeightedError::InvalidWeight)
);
}
#[test]
fn value_stability_choose() {
fn choose<I: Iterator<Item = u32>>(iter: I) -> Option<u32> {
let mut rng = crate::test::rng(411);
iter.choose(&mut rng)
}
assert_eq!(choose([].iter().cloned()), None);
assert_eq!(choose(0..100), Some(33));
assert_eq!(choose(UnhintedIterator { iter: 0..100 }), Some(40));
assert_eq!(
choose(ChunkHintedIterator {
iter: 0..100,
chunk_size: 32,
chunk_remaining: 32,
hint_total_size: false,
}),
Some(39)
);
assert_eq!(
choose(ChunkHintedIterator {
iter: 0..100,
chunk_size: 32,
chunk_remaining: 32,
hint_total_size: true,
}),
Some(39)
);
assert_eq!(
choose(WindowHintedIterator {
iter: 0..100,
window_size: 32,
hint_total_size: false,
}),
Some(90)
);
assert_eq!(
choose(WindowHintedIterator {
iter: 0..100,
window_size: 32,
hint_total_size: true,
}),
Some(90)
);
}
#[test]
fn value_stability_choose_stable() {
fn choose<I: Iterator<Item = u32>>(iter: I) -> Option<u32> {
let mut rng = crate::test::rng(411);
iter.choose_stable(&mut rng)
}
assert_eq!(choose([].iter().cloned()), None);
assert_eq!(choose(0..100), Some(40));
assert_eq!(choose(UnhintedIterator { iter: 0..100 }), Some(40));
assert_eq!(
choose(ChunkHintedIterator {
iter: 0..100,
chunk_size: 32,
chunk_remaining: 32,
hint_total_size: false,
}),
Some(40)
);
assert_eq!(
choose(ChunkHintedIterator {
iter: 0..100,
chunk_size: 32,
chunk_remaining: 32,
hint_total_size: true,
}),
Some(40)
);
assert_eq!(
choose(WindowHintedIterator {
iter: 0..100,
window_size: 32,
hint_total_size: false,
}),
Some(40)
);
assert_eq!(
choose(WindowHintedIterator {
iter: 0..100,
window_size: 32,
hint_total_size: true,
}),
Some(40)
);
}
#[test]
fn value_stability_choose_multiple() {
fn do_test<I: Iterator<Item = u32>>(iter: I, v: &[u32]) {
let mut rng = crate::test::rng(412);
let mut buf = [0u32; 8];
assert_eq!(iter.choose_multiple_fill(&mut rng, &mut buf), v.len());
assert_eq!(&buf[0..v.len()], v);
}
do_test(0..4, &[0, 1, 2, 3]);
do_test(0..8, &[0, 1, 2, 3, 4, 5, 6, 7]);
do_test(0..100, &[58, 78, 80, 92, 43, 8, 96, 7]);
#[cfg(feature = "alloc")]
{
fn do_test<I: Iterator<Item = u32>>(iter: I, v: &[u32]) {
let mut rng = crate::test::rng(412);
assert_eq!(iter.choose_multiple(&mut rng, v.len()), v);
}
do_test(0..4, &[0, 1, 2, 3]);
do_test(0..8, &[0, 1, 2, 3, 4, 5, 6, 7]);
do_test(0..100, &[58, 78, 80, 92, 43, 8, 96, 7]);
}
}
#[test]
#[cfg(feature = "std")]
fn test_multiple_weighted_edge_cases() {
use super::*;
let mut rng = crate::test::rng(413);
// Case 1: One of the weights is 0
let choices = [('a', 2), ('b', 1), ('c', 0)];
for _ in 0..100 {
let result = choices
.choose_multiple_weighted(&mut rng, 2, |item| item.1)
.unwrap()
.collect::<Vec<_>>();
assert_eq!(result.len(), 2);
assert!(!result.iter().any(|val| val.0 == 'c'));
}
// Case 2: All of the weights are 0
let choices = [('a', 0), ('b', 0), ('c', 0)];
assert_eq!(choices
.choose_multiple_weighted(&mut rng, 2, |item| item.1)
.unwrap().count(), 2);
// Case 3: Negative weights
let choices = [('a', -1), ('b', 1), ('c', 1)];
assert_eq!(
choices
.choose_multiple_weighted(&mut rng, 2, |item| item.1)
.unwrap_err(),
WeightedError::InvalidWeight
);
// Case 4: Empty list
let choices = [];
assert_eq!(choices
.choose_multiple_weighted(&mut rng, 0, |_: &()| 0)
.unwrap().count(), 0);
// Case 5: NaN weights
let choices = [('a', core::f64::NAN), ('b', 1.0), ('c', 1.0)];
assert_eq!(
choices
.choose_multiple_weighted(&mut rng, 2, |item| item.1)
.unwrap_err(),
WeightedError::InvalidWeight
);
// Case 6: +infinity weights
let choices = [('a', core::f64::INFINITY), ('b', 1.0), ('c', 1.0)];
for _ in 0..100 {
let result = choices
.choose_multiple_weighted(&mut rng, 2, |item| item.1)
.unwrap()
.collect::<Vec<_>>();
assert_eq!(result.len(), 2);
assert!(result.iter().any(|val| val.0 == 'a'));
}
// Case 7: -infinity weights
let choices = [('a', core::f64::NEG_INFINITY), ('b', 1.0), ('c', 1.0)];
assert_eq!(
choices
.choose_multiple_weighted(&mut rng, 2, |item| item.1)
.unwrap_err(),
WeightedError::InvalidWeight
);
// Case 8: -0 weights
let choices = [('a', -0.0), ('b', 1.0), ('c', 1.0)];
assert!(choices
.choose_multiple_weighted(&mut rng, 2, |item| item.1)
.is_ok());
}
#[test]
#[cfg(feature = "std")]
fn test_multiple_weighted_distributions() {
use super::*;
// The theoretical probabilities of the different outcomes are:
// AB: 0.5 * 0.5 = 0.250
// AC: 0.5 * 0.5 = 0.250
// BA: 0.25 * 0.67 = 0.167
// BC: 0.25 * 0.33 = 0.082
// CA: 0.25 * 0.67 = 0.167
// CB: 0.25 * 0.33 = 0.082
let choices = [('a', 2), ('b', 1), ('c', 1)];
let mut rng = crate::test::rng(414);
let mut results = [0i32; 3];
let expected_results = [4167, 4167, 1666];
for _ in 0..10000 {
let result = choices
.choose_multiple_weighted(&mut rng, 2, |item| item.1)
.unwrap()
.collect::<Vec<_>>();
assert_eq!(result.len(), 2);
match (result[0].0, result[1].0) {
('a', 'b') | ('b', 'a') => {
results[0] += 1;
}
('a', 'c') | ('c', 'a') => {
results[1] += 1;
}
('b', 'c') | ('c', 'b') => {
results[2] += 1;
}
(_, _) => panic!("unexpected result"),
}
}
let mut diffs = results
.iter()
.zip(&expected_results)
.map(|(a, b)| (a - b).abs());
assert!(!diffs.any(|deviation| deviation > 100));
}
}