fix(knn): accept k=1 in KNNClassifier

CHANGELOG.md

@@ -7,9 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [0.6.16]
 ### Fixed
 - `predict`, `predict_proba`, `predict_oob`, `predict_matrix` and `decision_function` now return `Err(Failed)` instead of panicking when `fit` has not run (#469, #470). Models return the same unfitted error even after deserialization, when the state fields are `None`. The guarded methods cover the decision trees, the random forests, the extra-trees regressor, the KNN classifier and regressor, `SVC`, `MultiClassSVC`, `SVR`, the linear models, the naive Bayes classifiers and `XGRegressor`.
+- `KNNClassifier::fit` now accepts `k = 1`, as `KNNRegressor` already did (#476). `k = 0` is still rejected.
 
 ### Changed
 - The error text for an unfitted `MultiClassSVC::predict` changed from "MultiClassSVC is not fitted" to the common "'fit' should be called before calling 'predict'". Code that matched the old string must be updated.
+- The error text for an invalid `k` in `KNNClassifier::fit` changed from "k should be > 1" to "k should be > 0", the same text `KNNRegressor` uses. Code that matched the old string must be updated.
 
 ## [0.6.15]
 ### Fixed

src/neighbors/knn_classifier.rs

@@ -241,9 +241,9 @@ impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec
             )));
         }
 
-        if parameters.k <= 1 {
+        if parameters.k < 1 {
             return Err(Failed::fit(&format!(
-                "k should be > 1, k=[{}]",
+                "k should be > 0, k=[{}]",
                 parameters.k
             )));
         }
@@ -421,6 +421,41 @@ mod tests {
         assert_eq!(vec![3], y_hat);
     }
 
+    #[test]
+    fn knn_fit_predict_k1() {
+        let x = DenseMatrix::from_2d_array(&[&[1.], &[2.], &[3.], &[4.], &[5.]]).unwrap();
+        let y = vec![2, 3, 2, 3, 2];
+        let one_hot = vec![
+            vec![1., 0.],
+            vec![0., 1.],
+            vec![1., 0.],
+            vec![0., 1.],
+            vec![1., 0.],
+        ];
+
+        for algorithm in [KNNAlgorithmName::CoverTree, KNNAlgorithmName::LinearSearch] {
+            let knn = KNNClassifier::fit(
+                &x,
+                &y,
+                KNNClassifierParameters::default()
+                    .with_k(1)
+                    .with_algorithm(algorithm),
+            )
+            .unwrap();
+
+            assert_eq!(y, knn.predict(&x).unwrap());
+            assert_eq!(one_hot, knn.predict_proba(&x).unwrap());
+        }
+    }
+
+    #[test]
+    fn knn_fit_k0_fails() {
+        let x = DenseMatrix::from_2d_array(&[&[1.], &[2.]]).unwrap();
+        let y = vec![2, 3];
+
+        assert!(KNNClassifier::fit(&x, &y, KNNClassifierParameters::default().with_k(0)).is_err());
+    }
+
     // New 8 tests (2026-03-19)
     #[test]
     fn knn_predict_proba_valid() {