mod.rs

// 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));
    }
}