chore: fix clippy (#283)

* chore: fix clippy


Co-authored-by: Luis Moreno <[email protected]>

Author: morenol

.github/workflows/ci.yml

@@ -36,7 +36,7 @@ jobs:
       - name: Install Rust toolchain
         uses: actions-rs/toolchain@v1
         with:
-          toolchain: stable
+          toolchain: 1.81 # 1.82 seems to break wasm32 tests https://github.com/rustwasm/wasm-bindgen/issues/4274
           target: ${{ matrix.platform.target }}
           profile: minimal
           default: true

src/algorithm/neighbour/cover_tree.rs

@@ -124,7 +124,7 @@ impl<T: Debug + PartialEq, D: Distance<T>> CoverTree<T, D> {
         current_cover_set.push((d, &self.root));
 
         let mut heap = HeapSelection::with_capacity(k);
-        heap.add(std::f64::MAX);
+        heap.add(f64::MAX);
 
         let mut empty_heap = true;
         if !self.identical_excluded || self.get_data_value(self.root.idx) != p {
@@ -145,7 +145,7 @@ impl<T: Debug + PartialEq, D: Distance<T>> CoverTree<T, D> {
                     }
 
                     let upper_bound = if empty_heap {
-                        std::f64::INFINITY
+                        f64::INFINITY
                     } else {
                         *heap.peek()
                     };
@@ -291,7 +291,7 @@ impl<T: Debug + PartialEq, D: Distance<T>> CoverTree<T, D> {
         } else {
             let max_dist = self.max(point_set);
             let next_scale = (max_scale - 1).min(self.get_scale(max_dist));
-            if next_scale == std::i64::MIN {
+            if next_scale == i64::MIN {
                 let mut children: Vec<Node> = Vec::new();
                 let mut leaf = self.new_leaf(p);
                 children.push(leaf);
@@ -435,7 +435,7 @@ impl<T: Debug + PartialEq, D: Distance<T>> CoverTree<T, D> {
 
     fn get_scale(&self, d: f64) -> i64 {
         if d == 0f64 {
-            std::i64::MIN
+            i64::MIN
         } else {
             (self.inv_log_base * d.ln()).ceil() as i64
         }

src/algorithm/neighbour/fastpair.rs

@@ -52,10 +52,8 @@ pub struct FastPair<'a, T: RealNumber + FloatNumber, M: Array2<T>> {
 }
 
 impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
-    ///
     /// Constructor
-    /// Instantiate and inizialise the algorithm
-    ///
+    /// Instantiate and initialize the algorithm
     pub fn new(m: &'a M) -> Result<Self, Failed> {
         if m.shape().0 < 3 {
             return Err(Failed::because(
@@ -74,10 +72,8 @@ impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
         Ok(init)
     }
 
-    ///
     /// Initialise `FastPair` by passing a `Array2`.
     /// Build a FastPairs data-structure from a set of (new) points.
-    ///
     fn init(&mut self) {
         // basic measures
         let len = self.samples.shape().0;
@@ -158,9 +154,7 @@ impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
         self.neighbours = neighbours;
     }
 
-    ///
     /// Find closest pair by scanning list of nearest neighbors.
-    ///
     #[allow(dead_code)]
     pub fn closest_pair(&self) -> PairwiseDistance<T> {
         let mut a = self.neighbours[0]; // Start with first point
@@ -217,9 +211,7 @@ mod tests_fastpair {
     use super::*;
     use crate::linalg::basic::{arrays::Array, matrix::DenseMatrix};
 
-    ///
     /// Brute force algorithm, used only for comparison and testing
-    ///
     pub fn closest_pair_brute(fastpair: &FastPair<f64, DenseMatrix<f64>>) -> PairwiseDistance<f64> {
         use itertools::Itertools;
         let m = fastpair.samples.shape().0;

src/algorithm/neighbour/linear_search.rs

@@ -61,7 +61,7 @@ impl<T, D: Distance<T>> LinearKNNSearch<T, D> {
 
         for _ in 0..k {
             heap.add(KNNPoint {
-                distance: std::f64::INFINITY,
+                distance: f64::INFINITY,
                 index: None,
             });
         }
@@ -215,7 +215,7 @@ mod tests {
         };
 
         let point_inf = KNNPoint {
-            distance: std::f64::INFINITY,
+            distance: f64::INFINITY,
             index: Some(3),
         };
 

src/algorithm/sort/heap_select.rs

@@ -133,7 +133,7 @@ mod tests {
     #[test]
     fn test_add1() {
         let mut heap = HeapSelection::with_capacity(3);
-        heap.add(std::f64::INFINITY);
+        heap.add(f64::INFINITY);
         heap.add(-5f64);
         heap.add(4f64);
         heap.add(-1f64);
@@ -151,7 +151,7 @@ mod tests {
     #[test]
     fn test_add2() {
         let mut heap = HeapSelection::with_capacity(3);
-        heap.add(std::f64::INFINITY);
+        heap.add(f64::INFINITY);
         heap.add(0.0);
         heap.add(8.4852);
         heap.add(5.6568);

src/algorithm/sort/quick_sort.rs

@@ -3,6 +3,7 @@ use num_traits::Num;
 pub trait QuickArgSort {
     fn quick_argsort_mut(&mut self) -> Vec<usize>;
 
+    #[allow(dead_code)]
     fn quick_argsort(&self) -> Vec<usize>;
 }
 

src/cluster/kmeans.rs

@@ -96,7 +96,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> PartialEq for KMeans<
                     return false;
                 }
                 for j in 0..self.centroids[i].len() {
-                    if (self.centroids[i][j] - other.centroids[i][j]).abs() > std::f64::EPSILON {
+                    if (self.centroids[i][j] - other.centroids[i][j]).abs() > f64::EPSILON {
                         return false;
                     }
                 }
@@ -270,7 +270,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
 
         let (n, d) = data.shape();
 
-        let mut distortion = std::f64::MAX;
+        let mut distortion = f64::MAX;
         let mut y = KMeans::<TX, TY, X, Y>::kmeans_plus_plus(data, parameters.k, parameters.seed);
         let mut size = vec![0; parameters.k];
         let mut centroids = vec![vec![0f64; d]; parameters.k];
@@ -331,7 +331,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
         let mut row = vec![0f64; x.shape().1];
 
         for i in 0..n {
-            let mut min_dist = std::f64::MAX;
+            let mut min_dist = f64::MAX;
             let mut best_cluster = 0;
 
             for j in 0..self.k {
@@ -361,7 +361,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
             .cloned()
             .collect();
 
-        let mut d = vec![std::f64::MAX; n];
+        let mut d = vec![f64::MAX; n];
         let mut row = vec![TX::zero(); data.shape().1];
 
         for j in 1..k {