replace vec.truncate(0) with vec.clear()

[nihav.git] / nihav-codec-support / src / vq / generic_elbg.rs

diff --git a/nihav-codec-support/src/vq/generic_elbg.rs b/nihav-codec-support/src/vq/generic_elbg.rs
index 87ea16e90560935816658f9ac5d9c29ddf770886..d6c30d2a6de791d9d39268ea7c8d17a05810a53d 100644 (file)
--- a/nihav-codec-support/src/vq/generic_elbg.rs
+++ b/nihav-codec-support/src/vq/generic_elbg.rs
@@ -148,7 +148,7 @@ impl<T: VQElement+Default, TS: VQElementSum<T>> ELBG<T, TS> {
         clu1.calc_dist();
         clu0.dist + clu1.dist
     }
-    #[allow(clippy::cyclomatic_complexity)]
+    #[allow(clippy::cognitive_complexity)]
     pub fn quantise(&mut self, src: &[T], dst: &mut [T]) -> usize {
         if src.is_empty() || dst.len() != self.clusters.len() {
             return 0;
@@ -199,7 +199,7 @@ impl<T: VQElement+Default, TS: VQElementSum<T>> ELBG<T, TS> {
             }
 
             // put points into the nearest clusters
-            indices.truncate(0);
+            indices.clear();
             for entry in entries.iter() {
                 let mut bestidx = 0;
                 let mut bestdist = std::u32::MAX;
@@ -230,8 +230,8 @@ impl<T: VQElement+Default, TS: VQElementSum<T>> ELBG<T, TS> {
             }
 
             let dmean = dist / (dst.len() as u64);
-            low_u.truncate(0);
-            high_u.truncate(0);
+            low_u.clear();
+            high_u.clear();
             let mut used = vec![false; dst.len()];
             for (i, cluster) in self.clusters.iter().enumerate() {
                 if cluster.dist < dmean {