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k_smallest.rs
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use alloc::vec::Vec;
use core::cmp::Ordering;
/// Consumes a given iterator, returning the minimum elements in **ascending** order.
pub(crate) fn k_smallest_general<I, F>(iter: I, k: usize, mut comparator: F) -> Vec<I::Item>
where
I: Iterator,
F: FnMut(&I::Item, &I::Item) -> Ordering,
{
/// Sift the element currently at `origin` away from the root until it is properly ordered.
///
/// This will leave **larger** elements closer to the root of the heap.
fn sift_down<T, F>(heap: &mut [T], is_less_than: &mut F, mut origin: usize)
where
F: FnMut(&T, &T) -> bool,
{
#[inline]
fn children_of(n: usize) -> (usize, usize) {
(2 * n + 1, 2 * n + 2)
}
while origin < heap.len() {
let (left_idx, right_idx) = children_of(origin);
if left_idx >= heap.len() {
return;
}
let replacement_idx =
if right_idx < heap.len() && is_less_than(&heap[left_idx], &heap[right_idx]) {
right_idx
} else {
left_idx
};
if is_less_than(&heap[origin], &heap[replacement_idx]) {
heap.swap(origin, replacement_idx);
origin = replacement_idx;
} else {
return;
}
}
}
if k == 0 {
iter.last();
return Vec::new();
}
if k == 1 {
return iter.min_by(comparator).into_iter().collect();
}
let mut iter = iter.fuse();
let mut storage: Vec<I::Item> = iter.by_ref().take(k).collect();
let mut is_less_than = move |a: &_, b: &_| comparator(a, b) == Ordering::Less;
// Rearrange the storage into a valid heap by reordering from the second-bottom-most layer up to the root.
// Slightly faster than ordering on each insert, but only by a factor of lg(k).
// The resulting heap has the **largest** item on top.
for i in (0..=(storage.len() / 2)).rev() {
sift_down(&mut storage, &mut is_less_than, i);
}
iter.for_each(|val| {
debug_assert_eq!(storage.len(), k);
if is_less_than(&val, &storage[0]) {
// Treating this as an push-and-pop saves having to write a sift-up implementation.
// https://en.wikipedia.org/wiki/Binary_heap#Insert_then_extract
storage[0] = val;
// We retain the smallest items we've seen so far, but ordered largest first so we can drop the largest efficiently.
sift_down(&mut storage, &mut is_less_than, 0);
}
});
// Ultimately the items need to be in least-first, strict order, but the heap is currently largest-first.
// To achieve this, repeatedly,
// 1) "pop" the largest item off the heap into the tail slot of the underlying storage,
// 2) shrink the logical size of the heap by 1,
// 3) restore the heap property over the remaining items.
let mut heap = &mut storage[..];
while heap.len() > 1 {
let last_idx = heap.len() - 1;
heap.swap(0, last_idx);
// Sifting over a truncated slice means that the sifting will not disturb already popped elements.
heap = &mut heap[..last_idx];
sift_down(heap, &mut is_less_than, 0);
}
storage
}
pub(crate) fn k_smallest_relaxed_general<I, F>(iter: I, k: usize, mut comparator: F) -> Vec<I::Item>
where
I: Iterator,
F: FnMut(&I::Item, &I::Item) -> Ordering,
{
if k == 0 {
iter.last();
return Vec::new();
}
let mut iter = iter.fuse();
let mut buf = iter.by_ref().take(2 * k).collect::<Vec<_>>();
if buf.len() < k {
buf.sort_unstable_by(&mut comparator);
return buf;
}
buf.select_nth_unstable_by(k - 1, &mut comparator);
buf.truncate(k);
iter.for_each(|val| {
if comparator(&val, &buf[k - 1]) != Ordering::Less {
return;
}
assert_ne!(buf.len(), buf.capacity());
buf.push(val);
if buf.len() == 2 * k {
buf.select_nth_unstable_by(k - 1, &mut comparator);
buf.truncate(k);
}
});
buf.sort_unstable_by(&mut comparator);
buf.truncate(k);
buf
}
#[inline]
pub(crate) fn key_to_cmp<T, K, F>(mut key: F) -> impl FnMut(&T, &T) -> Ordering
where
F: FnMut(&T) -> K,
K: Ord,
{
move |a, b| key(a).cmp(&key(b))
}