Simplify heuristic selection code

src/lib.rs

@@ -1,5 +1,5 @@
 use std::cmp::{max, min, Ordering, Reverse};
-use std::collections::{BinaryHeap, VecDeque};
+use std::collections::BinaryHeap;
 use std::hash::Hash;
 use std::ops::Index;
 
@@ -476,6 +476,8 @@ pub struct Search {
     candidates: BinaryHeap<Reverse<Candidate>>,
     /// Nearest neighbors found so far (`W` in the paper)
     nearest: Vec<Candidate>,
+    /// Working set for heuristic selection
+    working: Vec<Candidate>,
     /// Maximum number of nearest neighbors to retain (`ef` in the paper)
     ef: usize,
 }
@@ -487,12 +489,14 @@ impl Search {
             visited,
             candidates,
             nearest,
+            working,
             ef: _,
         } = self;
 
         visited.clear();
         candidates.clear();
         nearest.clear();
+        working.clear();
     }
 
     /// Selection of neighbors for insertion (algorithm 3 from the paper)
@@ -509,61 +513,35 @@ impl Search {
         points: &[P],
         params: Heuristic,
     ) -> &[Candidate] {
-        // Get input candidates from `self.nearest` and store them in `self.candidates`.
+        self.working.clear();
+        // Get input candidates from `self.nearest` and store them in `self.working`.
         // `self.candidates` will represent `W` from the paper's algorithm 4 for now.
-        self.candidates.clear();
+        for &candidate in &self.nearest {
-        self.nearest.sort_unstable();
+            self.working.push(candidate);
-        for &candidate in self.nearest.iter() {
+            if params.extend_candidates {
-            self.candidates.push(Reverse(candidate));
-        }
-        // Clear `self.nearest`. This now represents the result set (`R`).
-        self.nearest.clear();
-
-        let mut working = VecDeque::new();
-        if params.extend_candidates {
-            // Because we can't extend `self.candidates` while we iterate over it, we use
-            // `working` to accumulate candidates' neighbors in.
-            for Reverse(candidate) in &self.candidates {
                 for pid in layer.nearest_iter(candidate.pid).take(num) {
                     let other = &points[pid];
                     let distance = OrderedFloat::from(point.distance(other));
-                    working.push_back(Candidate { distance, pid });
+                    self.working.push(Candidate { distance, pid });
                 }
             }
-
-            // Once we have all the extended candidates, push them onto `self.candidates`.
-            // Because `self.candidates` is a `BinaryHeap`, it remains in sorted order.
-            // After this loop, `working` is empty again, so we can reuse it.
-            for candidate in working.drain(..) {
-                self.candidates.push(Reverse(candidate));
-            }
         }
 
-        // Take candidates from `self.candidates` (`W`) and compare them to the re
+        self.working.sort_unstable();
-        while let Some(Reverse(candidate)) = self.candidates.pop() {
+        self.nearest.clear();
-            if self.nearest.len() >= num {
+        self.nearest.push(self.working[0]);
-                break;
-            }
 
-            match self.nearest.binary_search(&candidate) {
-                Err(0) => self.nearest.insert(0, candidate),
-                Err(_) => working.push_back(candidate),
-                Ok(_) => unreachable!(),
-            }
-        }
-
-        // `working` was filled by pushing back from `candidates`, so it must be in sorted order.
         if params.keep_pruned {
-            while let Some(candidate) = working.pop_front() {
+            // Add discarded connections from `working` (`Wd`) to `self.nearest` (`R`)
+            for candidate in self.working.drain(1..) {
                 if self.nearest.len() >= num {
                     break;
                 }
-
                 self.nearest.push(candidate);
             }
         }
 
-        self.select_simple(num)
+        &self.nearest[..min(self.nearest.len(), num)]
     }
 
     /// Track node `pid` as a potential new neighbor for the given `point`
@@ -617,6 +595,7 @@ impl Default for Search {
             visited: HashSet::new(),
             candidates: BinaryHeap::new(),
             nearest: Vec::new(),
+            working: Vec::new(),
             ef: 1,
         }
     }