chore: update clippy lints (#272)

* chore: fix clippy lints
---------

Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>

src/cluster/dbscan.rs

@@ -315,8 +315,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
                         }
                     }
 
-                    while !neighbors.is_empty() {
+                    while let Some(neighbor) = neighbors.pop() {
-                        let neighbor = neighbors.pop().unwrap();
                         let index = neighbor.0;
 
                         if y[index] == outlier {

src/dataset/diabetes.rs

@@ -40,7 +40,7 @@ pub fn load_dataset() -> Dataset<f32, u32> {
         target: y,
         num_samples,
         num_features,
-        feature_names: vec![
+        feature_names: [
             "Age", "Sex", "BMI", "BP", "S1", "S2", "S3", "S4", "S5", "S6",
         ]
         .iter()

src/dataset/digits.rs

@@ -25,16 +25,14 @@ pub fn load_dataset() -> Dataset<f32, f32> {
         target: y,
         num_samples,
         num_features,
-        feature_names: vec![
+        feature_names: ["sepal length (cm)",
-            "sepal length (cm)",
             "sepal width (cm)",
             "petal length (cm)",
-            "petal width (cm)",
+            "petal width (cm)"]
-        ]
         .iter()
         .map(|s| s.to_string())
         .collect(),
-        target_names: vec!["setosa", "versicolor", "virginica"]
+        target_names: ["setosa", "versicolor", "virginica"]
             .iter()
             .map(|s| s.to_string())
             .collect(),

src/dataset/iris.rs

@@ -36,7 +36,7 @@ pub fn load_dataset() -> Dataset<f32, u32> {
         target: y,
         num_samples,
         num_features,
-        feature_names: vec![
+        feature_names: [
             "sepal length (cm)",
             "sepal width (cm)",
             "petal length (cm)",
@@ -45,7 +45,7 @@ pub fn load_dataset() -> Dataset<f32, u32> {
         .iter()
         .map(|s| s.to_string())
         .collect(),
-        target_names: vec!["setosa", "versicolor", "virginica"]
+        target_names: ["setosa", "versicolor", "virginica"]
             .iter()
             .map(|s| s.to_string())
             .collect(),

src/linalg/basic/arrays.rs

@@ -188,8 +188,7 @@ pub trait ArrayView1<T: Debug + Display + Copy + Sized>: Array<T, usize> {
                 _ => max,
             }
         };
-        self.iterator(0)
+        self.iterator(0).fold(T::min_value(), max_f)
-            .fold(T::min_value(), |max, x| max_f(max, x))
     }
     /// return min value from  the view
     fn min(&self) -> T
@@ -202,8 +201,7 @@ pub trait ArrayView1<T: Debug + Display + Copy + Sized>: Array<T, usize> {
                 _ => min,
             }
         };
-        self.iterator(0)
+        self.iterator(0).fold(T::max_value(), min_f)
-            .fold(T::max_value(), |max, x| min_f(max, x))
     }
     /// return the position of the max value of the view
     fn argmax(&self) -> usize

src/linalg/basic/matrix.rs

@@ -650,7 +650,7 @@ mod tests {
 
     #[test]
     fn test_from_iterator() {
-        let data = vec![1, 2, 3, 4, 5, 6];
+        let data = [1, 2, 3, 4, 5, 6];
 
         let m = DenseMatrix::from_iterator(data.iter(), 2, 3, 0);
 

src/linalg/basic/vector.rs

@@ -211,7 +211,7 @@ mod tests {
 
     #[test]
     fn test_len() {
-        let x = vec![1, 2, 3];
+        let x = [1, 2, 3];
         assert_eq!(3, x.len());
     }
 

src/linear/bg_solver.rs

@@ -160,7 +160,7 @@ mod tests {
     fn bg_solver() {
         let a = DenseMatrix::from_2d_array(&[&[25., 15., -5.], &[15., 18., 0.], &[-5., 0., 11.]]);
         let b = vec![40., 51., 28.];
-        let expected = vec![1.0, 2.0, 3.0];
+        let expected = [1.0, 2.0, 3.0];
 
         let mut x = Vec::zeros(3);
 

src/linear/logistic_regression.rs

@@ -890,11 +890,7 @@ mod tests {
 
         let y_hat = lr.predict(&x).unwrap();
 
-        let error: i32 = y
+        let error: i32 = y.into_iter().zip(y_hat).map(|(a, b)| (a - b).abs()).sum();
-            .into_iter()
-            .zip(y_hat.into_iter())
-            .map(|(a, b)| (a - b).abs())
-            .sum();
 
         assert!(error <= 1);
 

src/neighbors/knn_regressor.rs

@@ -297,7 +297,7 @@ mod tests {
         let x =
             DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
         let y: Vec<f64> = vec![1., 2., 3., 4., 5.];
-        let y_exp = vec![1., 2., 3., 4., 5.];
+        let y_exp = [1., 2., 3., 4., 5.];
         let knn = KNNRegressor::fit(
             &x,
             &y,
@@ -324,7 +324,7 @@ mod tests {
         let x =
             DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
         let y: Vec<f64> = vec![1., 2., 3., 4., 5.];
-        let y_exp = vec![2., 2., 3., 4., 4.];
+        let y_exp = [2., 2., 3., 4., 4.];
         let knn = KNNRegressor::fit(&x, &y, Default::default()).unwrap();
         let y_hat = knn.predict(&x).unwrap();
         assert_eq!(5, Vec::len(&y_hat));

src/preprocessing/categorical.rs

@@ -277,7 +277,7 @@ mod tests {
     )]
     #[test]
     fn hash_encode_f64_series() {
-        let series = vec![3.0, 1.0, 2.0, 1.0];
+        let series = [3.0, 1.0, 2.0, 1.0];
         let hashable_series: Vec<CategoricalFloat> =
             series.iter().map(|v| v.to_category()).collect();
         let enc = CategoryMapper::from_positional_category_vec(hashable_series);

src/svm/mod.rs

@@ -56,7 +56,7 @@ pub struct Kernels;
 impl Kernels {
     /// Return a default linear
     pub fn linear() -> LinearKernel {
-        LinearKernel::default()
+        LinearKernel
     }
     /// Return a default RBF
     pub fn rbf() -> RBFKernel {

src/tree/decision_tree_regressor.rs

@@ -767,7 +767,7 @@ mod tests {
             assert!((y_hat[i] - y[i]).abs() < 0.1);
         }
 
-        let expected_y = vec![
+        let expected_y = [
             87.3, 87.3, 87.3, 87.3, 98.9, 98.9, 98.9, 98.9, 98.9, 107.9, 107.9, 107.9, 114.85,
             114.85, 114.85, 114.85,
         ];
@@ -788,7 +788,7 @@ mod tests {
             assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
         }
 
-        let expected_y = vec![
+        let expected_y = [
             83.0, 88.35, 88.35, 89.5, 97.15, 97.15, 99.5, 99.5, 101.2, 104.6, 109.6, 109.6, 113.4,
             113.4, 116.30, 116.30,
         ];