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Diffstat (limited to 'rand/src/seq/mod.rs')
| -rw-r--r-- | rand/src/seq/mod.rs | 791 |
1 files changed, 0 insertions, 791 deletions
diff --git a/rand/src/seq/mod.rs b/rand/src/seq/mod.rs deleted file mode 100644 index cec9bb1..0000000 --- a/rand/src/seq/mod.rs +++ /dev/null @@ -1,791 +0,0 @@ -// 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 = "πππππ π’"; -/// println!("I am {}!", faces.chars().choose(&mut rng).unwrap()); -/// } -/// ``` -/// Example output (non-deterministic): -/// ```none -/// I am π! -/// ``` -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 - // [FisherβYates 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)); - } -} |