fix: formatting

src/linear/linear_regression.rs

@@ -26,7 +26,7 @@ pub struct LinearRegression<T: FloatExt, M: Matrix<T>> {
 impl Default for LinearRegressionParameters {
     fn default() -> Self {
         LinearRegressionParameters {
-            solver: LinearRegressionSolverName::SVD
+            solver: LinearRegressionSolverName::SVD,
         }
     }
 }
@@ -39,7 +39,11 @@ impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
 }
 
 impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
-    pub fn fit(x: &M, y: &M::RowVector, parameters: LinearRegressionParameters) -> LinearRegression<T, M> {
+    pub fn fit(
+        x: &M,
+        y: &M::RowVector,
+        parameters: LinearRegressionParameters,
+    ) -> LinearRegression<T, M> {
         let y_m = M::from_row_vector(y.clone());
         let b = y_m.transpose();
         let (x_nrows, num_attributes) = x.shape();
@@ -103,7 +107,14 @@ mod tests {
             114.2, 115.7, 116.9,
         ]);
 
-        let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionParameters{solver: LinearRegressionSolverName::QR}).predict(&x);
+        let y_hat_qr = LinearRegression::fit(
+            &x,
+            &y,
+            LinearRegressionParameters {
+                solver: LinearRegressionSolverName::QR,
+            },
+        )
+        .predict(&x);
 
         let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
 
@@ -143,7 +154,14 @@ mod tests {
             114.2, 115.7, 116.9,
         ];
 
-        let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionParameters{solver: LinearRegressionSolverName::QR}).predict(&x);
+        let y_hat_qr = LinearRegression::fit(
+            &x,
+            &y,
+            LinearRegressionParameters {
+                solver: LinearRegressionSolverName::QR,
+            },
+        )
+        .predict(&x);
 
         let y_hat_svd = LinearRegression::fit(&x, &y, Default::default()).predict(&x);
 

src/neighbors/knn.rs

@@ -15,7 +15,7 @@ pub enum KNNAlgorithmName {
 #[derive(Serialize, Deserialize, Debug)]
 pub struct KNNClassifierParameters {
     pub algorithm: KNNAlgorithmName,
-    pub k: usize
+    pub k: usize,
 }
 
 #[derive(Serialize, Deserialize, Debug)]
@@ -36,7 +36,7 @@ impl Default for KNNClassifierParameters {
     fn default() -> Self {
         KNNClassifierParameters {
             algorithm: KNNAlgorithmName::CoverTree,
-            k: 3
+            k: 3,
         }
     }
 }
@@ -93,7 +93,7 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
         x: &M,
         y: &M::RowVector,
         distance: D,
-        parameters: KNNClassifierParameters
+        parameters: KNNClassifierParameters,
     ) -> KNNClassifier<T, D> {
         let y_m = M::from_row_vector(y.clone());
 
@@ -118,7 +118,10 @@ impl<T: FloatExt, D: Distance<Vec<T>, T>> KNNClassifier<T, D> {
             )
         );
 
-        assert!(parameters.k > 1, format!("k should be > 1, k=[{}]", parameters.k));
+        assert!(
+            parameters.k > 1,
+            format!("k should be > 1, k=[{}]", parameters.k)
+        );
 
         KNNClassifier {
             classes: classes,
@@ -169,7 +172,10 @@ mod tests {
             &x,
             &y,
             Distances::euclidian(),
-            KNNClassifierParameters{k: 3, algorithm: KNNAlgorithmName::LinearSearch}            
+            KNNClassifierParameters {
+                k: 3,
+                algorithm: KNNAlgorithmName::LinearSearch,
+            },
         );
         let r = knn.predict(&x);
         assert_eq!(5, Vec::len(&r));
@@ -181,12 +187,7 @@ mod tests {
         let x = DenseMatrix::from_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);
         let y = vec![2., 2., 2., 3., 3.];
 
-        let knn = KNNClassifier::fit(
-            &x,
-            &y,            
-            Distances::euclidian(),
-            Default::default()
-        );
+        let knn = KNNClassifier::fit(&x, &y, Distances::euclidian(), Default::default());
 
         let deserialized_knn = bincode::deserialize(&bincode::serialize(&knn).unwrap()).unwrap();