chore: update clippy lints

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() {
- let neighbor = neighbors.pop().unwrap();
+ while let Some(neighbor) = neighbors.pop() {
  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![
- "sepal length (cm)",
+ feature_names: ["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)
- .fold(T::min_value(), |max, x| max_f(max, x))
+ self.iterator(0).fold(T::min_value(), max_f)
  }
  /// 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)
- .fold(T::max_value(), |max, x| min_f(max, x))
+ self.iterator(0).fold(T::max_value(), min_f)
  }
  /// 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
- .into_iter()
- .zip(y_hat.into_iter())
- .map(|(a, b)| (a - b).abs())
- .sum();
+ let error: i32 = y.into_iter().zip(y_hat).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,
  ];