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use packed_simd::{u128x4, u8x4, Cast};
pub struct FeatureHeap {
cap: usize,
size: usize,
in_search: usize,
search_distance: u32,
search: u128,
worst: u32,
features: [Vec<u128>; 129],
}
impl FeatureHeap {
pub fn new() -> Self {
Default::default()
}
/// Reset the heap while maintaining the allocated memory.
pub(crate) fn reset(&mut self, cap: usize, search: u128) {
assert_ne!(cap, 0);
self.cap = cap;
self.size = 0;
self.in_search = 0;
self.search_distance = 0;
self.search = search;
self.worst = 128;
for v in self.features.iter_mut() {
v.clear();
}
}
/// Update the minimum distance we are searching at.
pub(crate) fn search_distance(&mut self, distance: u32) {
assert!(distance >= self.search_distance);
self.in_search += self.features[self.search_distance as usize + 1..=distance as usize]
.iter()
.map(Vec::len)
.sum::<usize>();
self.search_distance = distance;
}
/// Add a feature to the search.
#[inline(always)]
pub(crate) fn add(&mut self, features: &[u128]) {
if self.size != self.cap {
// If we aren't at the cap, every new feature gets inserted,
// so SIMD would just slow us down.
for &feature in features {
self.add_one(feature);
}
} else {
let mut chunks = features.chunks_exact(4);
let search = u128x4::splat(self.search);
let mut worst = u8x4::splat(self.worst as u8);
for chunk in &mut chunks {
let feature = u128x4::from_slice_unaligned(chunk);
let distance: u8x4 = (feature ^ search).count_ones().cast();
// If anything is less than the worst.
if (distance - worst).bitmask() != 0 {
// Do the normal horizontal version.
for &feature in chunk {
self.add_one_cap(feature);
// Update the worst vector (since it may have changed).
worst = u8x4::splat(self.worst as u8);
}
}
}
for &feature in chunks.remainder() {
self.add_one_cap(feature);
}
}
}
/// Add a feature to the search.
#[inline(always)]
pub(crate) fn add_one(&mut self, feature: u128) {
let distance = (feature ^ self.search).count_ones();
// We stop searching once we have enough features under the search distance,
// so if this is true it will always get added to the FeatureHeap.
if distance <= self.search_distance {
self.in_search += 1;
}
if self.size != self.cap {
self.features[distance as usize].push(feature);
self.size += 1;
// Set the worst feature appropriately.
if self.size == self.cap {
self.update_worst();
}
} else if distance < self.worst {
self.features[distance as usize].push(feature);
self.remove_worst();
}
}
/// Add a feature to the search with the precondition we are already at the cap.
#[inline(always)]
fn add_one_cap(&mut self, feature: u128) {
let distance = (feature ^ self.search).count_ones();
// We stop searching once we have enough features under the search distance,
// so if this is true it will always get added to the FeatureHeap.
if distance < self.worst {
if distance <= self.search_distance {
self.in_search += 1;
}
self.features[distance as usize].push(feature);
self.remove_worst();
}
}
#[inline(always)]
fn update_worst(&mut self) {
self.worst -= self.features[0..=self.worst as usize]
.iter()
.rev()
.position(|v| !v.is_empty())
.unwrap() as u32;
}
#[inline(always)]
fn remove_worst(&mut self) {
self.features[self.worst as usize].pop();
self.update_worst();
}
#[inline(always)]
pub(crate) fn done(&self) -> bool {
self.in_search >= self.cap
}
pub(crate) fn fill_slice<'a>(&self, s: &'a mut [u128]) -> &'a mut [u128] {
let total_fill = std::cmp::min(s.len(), self.size);
for (ix, &f) in self
.features
.iter()
.flat_map(|v| v.iter())
.take(total_fill)
.enumerate()
{
s[ix] = f;
}
&mut s[0..total_fill]
}
}
impl Default for FeatureHeap {
fn default() -> Self {
Self {
cap: 0,
size: 0,
in_search: 0,
search_distance: 0,
search: 0,
worst: 128,
features: [
vec![],
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}
}
}