fix: formatting

src/model_selection/mod.rs

@@ -155,7 +155,7 @@ pub fn cross_val_predict<T, M, H, E, K, F>(
     x: &M,
     y: &M::RowVector,
     parameters: H,
-    cv: K
+    cv: K,
 ) -> Result<M::RowVector, Failed>
 where
     T: RealNumber,
@@ -163,14 +163,14 @@ where
     H: Clone,
     E: Predictor<M, M::RowVector>,
     K: BaseKFold,
-    F: Fn(&M, &M::RowVector, H) -> Result<E, Failed>
-{    
-    let mut y_hat = M::RowVector::zeros(y.len());    
-    
+    F: Fn(&M, &M::RowVector, H) -> Result<E, Failed>,
+{
+    let mut y_hat = M::RowVector::zeros(y.len());
+
     for (train_idx, test_idx) in cv.split(x) {
         let train_x = x.take(&train_idx, 0);
         let train_y = y.take(&train_idx);
-        let test_x = x.take(&test_idx, 0);                 
+        let test_x = x.take(&test_idx, 0);
 
         let estimator = fit_estimator(&train_x, &train_y, parameters.clone())?;
 
@@ -348,16 +348,8 @@ mod tests {
             ..KFold::default()
         };
 
-        let y_hat = cross_val_predict(
-            KNNRegressor::fit,
-            &x,
-            &y,
-            Default::default(),
-            cv            
-        )
-        .unwrap();        
+        let y_hat = cross_val_predict(KNNRegressor::fit, &x, &y, Default::default(), cv).unwrap();
 
         assert!(mean_absolute_error(&y, &y_hat) < 10.0);
     }
-
 }