fmt

Author: Mec-iS

src/naive_bayes/gaussian.rs

@@ -427,22 +427,18 @@ mod tests {
 
     #[test]
     fn run_gaussian_naive_bayes_with_few_samples() {
-        let x = DenseMatrix::<f64>::from_2d_array(&[
-            &[-1., -1.],
-            &[-2., -1.],
-            &[-3., -2.],
-            &[1., 1.],
-        ]);
+        let x =
+            DenseMatrix::<f64>::from_2d_array(&[&[-1., -1.], &[-2., -1.], &[-3., -2.], &[1., 1.]]);
         let y: Vec<u32> = vec![1, 1, 1, 2];
 
         let gnb = GaussianNB::fit(&x, &y, Default::default());
 
         match gnb.unwrap().predict(&x) {
             Ok(_) => assert!(false, "test should return Failed"),
             Err(err) => {
-                assert!(err.to_string() == "Can't find solution: log_likelihood for distribution of one of the rows is NaN"); 
+                assert!(err.to_string() == "Can't find solution: log_likelihood for distribution of one of the rows is NaN");
                 assert!(true)
-            },
+            }
         }
     }
 

src/naive_bayes/mod.rs

@@ -101,17 +101,17 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: NBDistribution<TX,
                     .iter()
                     .enumerate()
                     .map(|(class_index, class)| {
-                        let mut log_likelihood = self.distribution.log_likelihood(class_index, &row);
+                        let mut log_likelihood =
+                            self.distribution.log_likelihood(class_index, &row);
                         if log_likelihood.is_nan() {
                             log_likelihood = 0f64;
                             log_likehood_is_nan = true;
                         }
                         (
                             class,
-                            log_likelihood
-                                + self.distribution.prior(class_index).ln(),
+                            log_likelihood + self.distribution.prior(class_index).ln(),
                         )
-                      })
+                    })
                     .max_by(|(_, p1), (_, p2)| p1.partial_cmp(p2).unwrap())
                     .unwrap();
                 *prediction