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instant-distance
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Simplify selection sizes

This commit is contained in:
Dirkjan Ochtman 2021-01-21 10:20:52 +01:00
parent f627769667
commit 96b69e5d4b
1 changed files with 11 additions and 14 deletions
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25
src/lib.rs
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@ -352,12 +352,11 @@ where
}
}
let nearest = search.select_simple();
let found = min(nearest.len(), out.len());
for (i, candidate) in nearest.iter().take(found).enumerate() {
let nearest = search.select_simple(out.len());
for (i, candidate) in nearest.iter().enumerate() {
out[i] = candidate.pid;
}
found
nearest.len()
}
/// Iterate over the keys and values in this index
@ -389,8 +388,8 @@ fn insert<P: Point>(
) {
layer.push(ZeroNode::default());
let found = match heuristic {
None => search.select_simple(),
Some(heuristic) => search.select_heuristic(&points[new], &layer, M * 2, points, *heuristic),
None => search.select_simple(M * 2),
Some(heuristic) => search.select_heuristic(&points[new], &layer, points, *heuristic),
};
// Just make sure the candidates are all unique
@ -399,7 +398,7 @@ fn insert<P: Point>(
found.iter().map(|c| c.pid).collect::<HashSet<_>>().len()
);
for (i, candidate) in found.iter().take(M * 2).enumerate() {
for (i, candidate) in found.iter().enumerate() {
// `candidate` here is the new node's neighbor
let &Candidate { distance, pid } = candidate;
if let Some(heuristic) = heuristic {
@ -407,7 +406,6 @@ fn insert<P: Point>(
new,
layer.as_slice().nearest_iter(pid),
layer,
M * 2,
&points[pid],
points,
*heuristic,
@ -523,7 +521,6 @@ impl Search {
new: PointId,
current: impl Iterator<Item = PointId>,
layer: &[ZeroNode],
num: usize,
point: &P,
points: &[P],
params: Heuristic,
@ -533,7 +530,7 @@ impl Search {
for pid in current {
self.push(pid, point, points);
}
self.select_heuristic(point, &layer, num, points, params)
self.select_heuristic(point, &layer, points, params)
}
/// Heuristically sort and truncate neighbors in `self.nearest`
@ -543,7 +540,6 @@ impl Search {
&mut self,
point: &P,
layer: &[ZeroNode],
num: usize,
points: &[P],
params: Heuristic,
) -> &[Candidate] {
@ -573,7 +569,7 @@ impl Search {
self.nearest.clear();
self.discarded.clear();
for candidate in self.working.drain(..) {
if self.nearest.len() >= num {
if self.nearest.len() >= M * 2 {
break;
}
@ -594,7 +590,7 @@ impl Search {
if params.keep_pruned {
// Add discarded connections from `working` (`Wd`) to `self.nearest` (`R`)
for candidate in self.discarded.drain(..) {
if self.nearest.len() >= num {
if self.nearest.len() >= M * 2 {
break;
}
self.nearest.push(candidate);
@ -662,7 +658,8 @@ impl Search {
}
/// Selection of neighbors for insertion (algorithm 3 from the paper)
fn select_simple(&self) -> &[Candidate] {
fn select_simple(&mut self, num: usize) -> &[Candidate] {
self.nearest.truncate(num);
&self.nearest
}
}