Implement SVR and SVR kernels with Enum. Add tests for argsort_mut

.github/CODEOWNERS

@@ -2,6 +2,5 @@
 # the repo. Unless a later match takes precedence,
 # Developers in this list will be requested for
 # review when someone opens a pull request.
-*       @VolodymyrOrlov
 *       @morenol
 *       @Mec-iS

.github/CONTRIBUTING.md

@@ -50,9 +50,9 @@ $ rust-code-analysis-cli -p src/algorithm/neighbour/fastpair.rs --ls 22 --le 213
 
 1. After a PR is opened maintainers are notified
 2. Probably changes will be required to comply with the workflow, these commands are run automatically and all tests shall pass:
-    * **Coverage** (optional): `tarpaulin` is used with command `cargo tarpaulin --out Lcov --all-features -- --test-threads 1`
     * **Formatting**: run `rustfmt src/*.rs` to apply automatic formatting
     * **Linting**: `clippy` is used with command `cargo clippy --all-features -- -Drust-2018-idioms -Dwarnings`
+    * **Coverage** (optional): `tarpaulin` is used with command `cargo tarpaulin --out Lcov --all-features -- --test-threads 1`
     * **Testing**: multiple test pipelines are run for different targets
 3. When everything is OK, code is merged.
 

Cargo.toml

@@ -2,7 +2,7 @@
 name = "smartcore"
 description = "Machine Learning in Rust."
 homepage = "https://smartcorelib.org"
-version = "0.4.1"
+version = "0.4.2"
 authors = ["smartcore Developers"]
 edition = "2021"
 license = "Apache-2.0"

src/linalg/basic/arrays.rs

@@ -619,7 +619,7 @@ pub trait MutArrayView1<T: Debug + Display + Copy + Sized>:
         T: Number + PartialOrd,
     {
         let stack_size = 64;
-        let mut jstack = -1;
+        let mut jstack: i32 = -1;
         let mut l = 0;
         let mut istack = vec![0; stack_size];
         let mut ir = self.shape() - 1;
@@ -2190,4 +2190,29 @@ mod tests {
 
         assert_eq!(result, [65, 581, 30])
     }
+
+    #[test]
+    fn test_argsort_mut_exact_boundary() {
+        // Test index == length - 1 case
+        let boundary =
+            DenseMatrix::from_2d_array(&[&[1.0, 2.0, 3.0, f64::MAX], &[3.0, f64::MAX, 0.0, 2.0]])
+                .unwrap();
+        let mut view0: Vec<f64> = boundary.get_col(0).iterator(0).copied().collect();
+        let indices = view0.argsort_mut();
+        assert_eq!(indices.last(), Some(&1));
+        assert_eq!(indices.first(), Some(&0));
+
+        let mut view1: Vec<f64> = boundary.get_col(3).iterator(0).copied().collect();
+        let indices = view1.argsort_mut();
+        assert_eq!(indices.last(), Some(&0));
+        assert_eq!(indices.first(), Some(&1));
+    }
+
+    #[test]
+    fn test_argsort_mut_filled_array() {
+        let matrix = DenseMatrix::<f64>::rand(1000, 1000);
+        let mut view: Vec<f64> = matrix.get_col(0).iterator(0).copied().collect();
+        let sorted = view.argsort_mut();
+        assert_eq!(sorted.len(), 1000);
+    }
 }

src/neighbors/mod.rs

@@ -64,7 +64,7 @@ impl KNNWeightFunction {
             KNNWeightFunction::Distance => {
                 // if there are any points that has zero distance from one or more training points,
                 // those training points are weighted as 1.0 and the other points as 0.0
-                if distances.iter().any(|&e| e == 0f64) {
+                if distances.contains(&0f64) {
                     distances
                         .iter()
                         .map(|e| if *e == 0f64 { 1f64 } else { 0f64 })