style: include redundant_clone (#854)

Author: vil02

Cargo.toml

@@ -160,7 +160,6 @@ imprecise_flops = { level = "allow", priority = 1 }
 missing_const_for_fn = { level = "allow", priority = 1 }
 nonstandard_macro_braces = { level = "allow", priority = 1 }
 option_if_let_else = { level = "allow", priority = 1 }
-redundant_clone = { level = "allow", priority = 1 }
 suboptimal_flops = { level = "allow", priority = 1 }
 suspicious_operation_groupings = { level = "allow", priority = 1 }
 use_self = { level = "allow", priority = 1 }

src/graph/decremental_connectivity.rs

@@ -224,7 +224,7 @@ mod tests {
             HashSet::from([7, 8]),
             HashSet::from([7]),
         ];
-        let mut dec_con = super::DecrementalConnectivity::new(adjacent.clone()).unwrap();
+        let mut dec_con = super::DecrementalConnectivity::new(adjacent).unwrap();
         dec_con.delete(2, 4);
     }
 
@@ -260,7 +260,7 @@ mod tests {
         dec_con2.delete(4, 1);
         assert!(!dec_con2.connected(1, 4).unwrap());
 
-        let mut dec_con3 = super::DecrementalConnectivity::new(adjacent.clone()).unwrap();
+        let mut dec_con3 = super::DecrementalConnectivity::new(adjacent).unwrap();
         dec_con3.delete(1, 4);
         assert!(!dec_con3.connected(4, 1).unwrap());
     }

src/machine_learning/cholesky.rs

@@ -38,7 +38,7 @@ mod tests {
     fn test_cholesky() {
         // Test case 1
         let mat1 = vec![25.0, 15.0, -5.0, 15.0, 18.0, 0.0, -5.0, 0.0, 11.0];
-        let res1 = cholesky(mat1.clone(), 3);
+        let res1 = cholesky(mat1, 3);
 
         // The expected Cholesky decomposition values
         #[allow(clippy::useless_vec)]
@@ -92,7 +92,7 @@ mod tests {
     #[test]
     fn matrix_with_all_zeros() {
         let mat3 = vec![0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0];
-        let res3 = cholesky(mat3.clone(), 3);
+        let res3 = cholesky(mat3, 3);
         let expected3 = vec![0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0];
         assert_eq!(res3, expected3);
     }

src/math/softmax.rs

@@ -20,7 +20,7 @@
 use std::f32::consts::E;
 
 pub fn softmax(array: Vec<f32>) -> Vec<f32> {
-    let mut softmax_array = array.clone();
+    let mut softmax_array = array;
 
     for value in &mut softmax_array {
         *value = E.powf(*value);