format

src/algorithm/neighbour/fastpair.rs

@@ -212,7 +212,9 @@ mod tests_fastpair {
     use crate::linalg::basic::{arrays::Array, matrix::DenseMatrix};
 
     /// Brute force algorithm, used only for comparison and testing
-    pub fn closest_pair_brute(fastpair: &FastPair<'_, f64, DenseMatrix<f64>>) -> PairwiseDistance<f64> {
+    pub fn closest_pair_brute(
+        fastpair: &FastPair<'_, f64, DenseMatrix<f64>>,
+    ) -> PairwiseDistance<f64> {
         use itertools::Itertools;
         let m = fastpair.samples.shape().0;
 

src/linalg/basic/matrix.rs

@@ -579,9 +579,7 @@ impl<T: Debug + Display + Copy + Sized> Array<T, (usize, usize)> for DenseMatrix
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
-    for DenseMatrixMutView<'_, T>
-{
+impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)> for DenseMatrixMutView<'_, T> {
     fn set(&mut self, pos: (usize, usize), x: T) {
         if self.column_major {
             self.values[pos.0 + pos.1 * self.stride] = x;

src/linalg/ndarray/matrix.rs

@@ -146,9 +146,7 @@ impl<T: Number + RealNumber> SVDDecomposable<T> for ArrayBase<OwnedRepr<T>, Ix2>
 
 impl<T: Debug + Display + Copy + Sized> ArrayView2<T> for ArrayView<'_, T, Ix2> {}
 
-impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
-    for ArrayViewMut<'_, T, Ix2>
-{
+impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)> for ArrayViewMut<'_, T, Ix2> {
     fn get(&self, pos: (usize, usize)) -> &T {
         &self[[pos.0, pos.1]]
     }
@@ -175,9 +173,7 @@ impl<T: Debug + Display + Copy + Sized> BaseArray<T, (usize, usize)>
     }
 }
 
-impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)>
-    for ArrayViewMut<'_, T, Ix2>
-{
+impl<T: Debug + Display + Copy + Sized> MutArray<T, (usize, usize)> for ArrayViewMut<'_, T, Ix2> {
     fn set(&mut self, pos: (usize, usize), x: T) {
         self[[pos.0, pos.1]] = x
     }

src/preprocessing/numerical.rs

@@ -172,12 +172,14 @@ where
     T: Number + RealNumber,
     M: Array2<T>,
 {
-    columns.first().cloned().map(|output_matrix| columns
-                .iter()
-                .skip(1)
-                .fold(output_matrix, |current_matrix, new_colum| {
-                    current_matrix.h_stack(new_colum)
-                }))
+    columns.first().cloned().map(|output_matrix| {
+        columns
+            .iter()
+            .skip(1)
+            .fold(output_matrix, |current_matrix, new_colum| {
+                current_matrix.h_stack(new_colum)
+            })
+    })
 }
 
 #[cfg(test)]

src/tree/decision_tree_classifier.rs

@@ -77,9 +77,9 @@ use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
+use crate::linalg::basic::arrays::MutArray;
 use crate::linalg::basic::arrays::{Array1, Array2, MutArrayView1};
 use crate::linalg::basic::matrix::DenseMatrix;
-use crate::linalg::basic::arrays::MutArray;
 use crate::numbers::basenum::Number;
 use crate::rand_custom::get_rng_impl;
 
@@ -890,7 +890,6 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         importances
     }
 
-
     /// Predict class probabilities for the input samples.
     ///
     /// # Arguments
@@ -933,25 +932,25 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
     /// of the input sample belonging to each class.
     fn predict_proba_for_row(&self, x: &X, row: usize) -> Vec<f64> {
         let mut node = 0;
-        
+
         while let Some(current_node) = self.nodes().get(node) {
             if current_node.true_child.is_none() && current_node.false_child.is_none() {
                 // Leaf node reached
                 let mut probs = vec![0.0; self.classes().len()];
                 probs[current_node.output] = 1.0;
                 return probs;
             }
-            
+
             let split_feature = current_node.split_feature;
             let split_value = current_node.split_value.unwrap_or(f64::NAN);
-            
+
             if x.get((row, split_feature)).to_f64().unwrap() <= split_value {
                 node = current_node.true_child.unwrap();
             } else {
                 node = current_node.false_child.unwrap();
             }
         }
-        
+
         // This should never happen if the tree is properly constructed
         vec![0.0; self.classes().len()]
     }
@@ -960,8 +959,8 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::linalg::basic::matrix::DenseMatrix;
     use crate::linalg::basic::arrays::Array;
+    use crate::linalg::basic::matrix::DenseMatrix;
 
     #[test]
     fn search_parameters() {
@@ -1020,24 +1019,28 @@ mod tests {
             &[6.9, 3.1, 4.9, 1.5],
             &[5.5, 2.3, 4.0, 1.3],
             &[6.5, 2.8, 4.6, 1.5],
-        ]).unwrap();
+        ])
+        .unwrap();
         let y: Vec<usize> = vec![0, 0, 0, 0, 0, 1, 1, 1, 1, 1];
-    
+
         let tree = DecisionTreeClassifier::fit(&x, &y, Default::default()).unwrap();
         let probabilities = tree.predict_proba(&x).unwrap();
-    
+
         assert_eq!(probabilities.shape(), (10, 2));
-    
+
         for row in 0..10 {
             let row_sum: f64 = probabilities.get_row(row).sum();
-            assert!((row_sum - 1.0).abs() < 1e-6, "Row probabilities should sum to 1");
+            assert!(
+                (row_sum - 1.0).abs() < 1e-6,
+                "Row probabilities should sum to 1"
+            );
         }
-    
+
         // Check if the first 5 samples have higher probability for class 0
         for i in 0..5 {
             assert!(probabilities.get((i, 0)) > probabilities.get((i, 1)));
         }
-    
+
         // Check if the last 5 samples have higher probability for class 1
         for i in 5..10 {
             assert!(probabilities.get((i, 1)) > probabilities.get((i, 0)));