Another format.

src/ensemble/random_forest_regressor.rs

@@ -203,7 +203,11 @@ impl<T: RealNumber> RandomForestRegressor<T> {
             trees.push(tree);
         }
 
-        Ok(RandomForestRegressor { parameters, trees, samples: maybe_all_samples })
+        Ok(RandomForestRegressor {
+            parameters,
+            trees,
+            samples: maybe_all_samples,
+        })
     }
 
     /// Predict class for `x`
@@ -232,7 +236,6 @@ impl<T: RealNumber> RandomForestRegressor<T> {
         result / T::from(n_trees).unwrap()
     }
 
-
     /// Predict OOB classes for `x`. `x` is expected to be equal to the dataset used in training.
     pub fn predict_oob<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed> {
         let (n, _) = x.shape();
@@ -370,7 +373,8 @@ mod tests {
                 m: Option::None,
                 keep_samples: true,
             },
-        ).unwrap();
+        )
+        .unwrap();
 
         let y_hat = regressor.predict(&x).unwrap();
         let y_hat_oob = regressor.predict_oob(&x).unwrap();