Update Cargo.toml (#299)

* Update Cargo.toml

* chore: fix clippy

* chore: bump actions

* chore: fix clippy

* chore: update target name

---------

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

src/linalg/basic/matrix.rs

@@ -663,6 +663,7 @@ mod tests {
     #[test]
     fn test_instantiate_err_view3() {
         let x = DenseMatrix::from_2d_array(&[&[1., 2., 3.], &[4., 5., 6.], &[7., 8., 9.]]).unwrap();
+        #[allow(clippy::reversed_empty_ranges)]
         let v = DenseMatrixView::new(&x, 0..3, 4..3);
         assert!(v.is_err());
     }

src/naive_bayes/bernoulli.rs

@@ -257,8 +257,7 @@ impl<TY: Number + Ord + Unsigned> BernoulliNBDistribution<TY> {
     /// Fits the distribution to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data.
     /// * `y` - vector with target values (classes) of length N.
-    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
-    ///     priors are adjusted according to the data.
+    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined, priors are adjusted according to the data.
     /// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
     /// * `binarize` - Threshold for binarizing.
     fn fit<TX: Number + PartialOrd, X: Array2<TX>, Y: Array1<TY>>(

src/naive_bayes/gaussian.rs

@@ -174,8 +174,7 @@ impl<TY: Number + Ord + Unsigned> GaussianNBDistribution<TY> {
     /// Fits the distribution to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data.
     /// * `y` - vector with target values (classes) of length N.
-    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
-    ///     priors are adjusted according to the data.
+    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined, priors are adjusted according to the data.
     pub fn fit<TX: Number + RealNumber, X: Array2<TX>, Y: Array1<TY>>(
         x: &X,
         y: &Y,

src/naive_bayes/multinomial.rs

@@ -207,8 +207,7 @@ impl<TY: Number + Ord + Unsigned> MultinomialNBDistribution<TY> {
     /// Fits the distribution to a NxM matrix where N is number of samples and M is number of features.
     /// * `x` - training data.
     /// * `y` - vector with target values (classes) of length N.
-    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
-    ///     priors are adjusted according to the data.
+    /// * `priors` - Optional vector with prior probabilities of the classes. If not defined, priors are adjusted according to the data.
     /// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
     pub fn fit<TX: Number + Unsigned, X: Array2<TX>, Y: Array1<TY>>(
         x: &X,

src/preprocessing/categorical.rs

@@ -24,7 +24,7 @@
 //! //    &[1.5, 1.0, 0.0, 1.5, 0.0, 0.0, 1.0, 0.0]
 //! //    &[1.5, 0.0, 1.0, 1.5, 0.0, 0.0, 0.0, 1.0]
 //! ```
-use std::iter;
+use std::iter::repeat_n;
 
 use crate::error::Failed;
 use crate::linalg::basic::arrays::Array2;
@@ -75,11 +75,7 @@ fn find_new_idxs(num_params: usize, cat_sizes: &[usize], cat_idxs: &[usize]) ->
     let offset = (0..1).chain(offset_);
 
     let new_param_idxs: Vec<usize> = (0..num_params)
-        .zip(
-            repeats
-                .zip(offset)
-                .flat_map(|(r, o)| iter::repeat(o).take(r)),
-        )
+        .zip(repeats.zip(offset).flat_map(|(r, o)| repeat_n(o, r)))
         .map(|(idx, ofst)| idx + ofst)
         .collect();
     new_param_idxs
@@ -124,7 +120,7 @@ impl OneHotEncoder {
                 let (nrows, _) = data.shape();
 
                 // col buffer to avoid allocations
-                let mut col_buf: Vec<T> = iter::repeat(T::zero()).take(nrows).collect();
+                let mut col_buf: Vec<T> = repeat_n(T::zero(), nrows).collect();
 
                 let mut res: Vec<CategoryMapper<CategoricalFloat>> = Vec::with_capacity(idxs.len());