| // 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: |
| //! |
| //! * [`seq::SliceRandom`] slice sampling and mutation |
| //! * [`seq::IteratorRandom`] iterator sampling |
| //! * [`seq::index::sample`] low-level API to choose multiple indices from |
| //! `0..length` |
| //! |
| //! Also see: |
| //! |
| //! * [`distributions::weighted`] module which provides implementations of |
| //! 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")] pub mod index; |
| |
| #[cfg(feature="alloc")] use core::ops::Index; |
| |
| #[cfg(all(feature="alloc", not(feature="std")))] use crate::alloc::vec::Vec; |
| |
| use crate::Rng; |
| #[cfg(feature="alloc")] use crate::distributions::WeightedError; |
| #[cfg(feature="alloc")] use crate::distributions::uniform::{SampleUniform, SampleBorrow}; |
| |
| /// 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; |
| /// |
| /// fn main() { |
| /// 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")] |
| 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")] |
| 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")] |
| 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; |
| |
| /// 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 sized iterators, providing methods for |
| /// choosing one or more elements. You must `use` this trait: |
| /// |
| /// ``` |
| /// use rand::seq::IteratorRandom; |
| /// |
| /// fn main() { |
| /// 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. |
| /// |
| /// For slices, prefer [`SliceRandom::choose`] which guarantees `O(1)` |
| /// performance. |
| 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; |
| |
| 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; |
| let denom = consumed as f64; // accurate to 2^53 elements |
| if rng.gen_bool(1.0 / denom) { |
| result = elem; |
| } |
| } |
| |
| let hint = self.size_hint(); |
| lower = hint.0; |
| upper = hint.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")] |
| 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)]) |
| } |
| |
| 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")] |
| #[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); |
| } |
| |
| #[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(not(miri))] // 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(not(miri))] // 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 in 0..4 { |
| assert_eq!(arr[i], 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(not(miri))] // 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)); |
| } |
| } |
