Add getter for parameters in all model structs.

src/cluster/agglomerative.rs

@@ -76,6 +76,7 @@ impl Default for AgglomerativeClusteringParameters {
 pub struct AgglomerativeClustering<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> {
     /// The cluster label assigned to each sample.
     pub labels: Vec<usize>,
+    parameters: Option<AgglomerativeClusteringParameters>,
     _phantom_tx: PhantomData<TX>,
     _phantom_ty: PhantomData<TY>,
     _phantom_x: PhantomData<X>,
@@ -176,12 +177,21 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> AgglomerativeClusteri
         }
         Ok(AgglomerativeClustering {
             labels,
+            parameters: Some(parameters),
             _phantom_tx: PhantomData,
             _phantom_ty: PhantomData,
             _phantom_x: PhantomData,
             _phantom_y: PhantomData,
         })
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// `Some` with the parameters used to configure the model, or `None` if unavailable.
+    pub fn parameters(&self) -> Option<&AgglomerativeClusteringParameters> {
+        self.parameters.as_ref()
+    }
 }
 
 impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>>
@@ -314,4 +324,35 @@ mod tests {
 
         assert!(result.is_err());
     }
+
+    #[test]
+    fn test_can_get_assigned_parameters() {
+        let data = vec![0.0, 0.0, 5.0, 5.0, 10.0, 10.0];
+        let matrix = DenseMatrix::new(3, 2, data, false).unwrap();
+        let parameters = AgglomerativeClusteringParameters::default().with_n_clusters(1);
+        let expected_parameters = parameters.clone();
+        let clustering = AgglomerativeClustering::<f64, f64, DenseMatrix<f64>, Vec<f64>>::fit(
+            &matrix, parameters,
+        )
+        .unwrap();
+
+        let actual_parameters = clustering
+            .parameters()
+            .expect("parameters should be set after fitting");
+        assert_eq!(actual_parameters.n_clusters, expected_parameters.n_clusters);
+    }
+
+    #[test]
+    fn test_returns_none_on_no_parameters() {
+        let clustering = AgglomerativeClustering::<f64, f64, DenseMatrix<f64>, Vec<f64>> {
+            labels: Vec::new(),
+            parameters: None,
+            _phantom_tx: PhantomData,
+            _phantom_ty: PhantomData,
+            _phantom_x: PhantomData,
+            _phantom_y: PhantomData,
+        };
+
+        assert!(clustering.parameters().is_none());
+    }
 }

src/cluster/dbscan.rs

@@ -64,12 +64,13 @@ pub struct DBSCAN<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Dista
     num_classes: usize,
     knn_algorithm: KNNAlgorithm<TX, D>,
     eps: f64,
+    parameters: Option<DBSCANParameters<TX, D>>,
     _phantom_ty: PhantomData<TY>,
     _phantom_x: PhantomData<X>,
     _phantom_y: PhantomData<Y>,
 }
 
-#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[cfg_attr(feature = "serde", derive(Serialize))]
 #[derive(Debug, Clone)]
 /// DBSCAN clustering algorithm parameters
 pub struct DBSCANParameters<T: Number, D: Distance<Vec<T>>> {
@@ -91,6 +92,43 @@ pub struct DBSCANParameters<T: Number, D: Distance<Vec<T>>> {
     _phantom_t: PhantomData<T>,
 }
 
+// A manual implementation avoids adding an unnecessary `Default` bound to
+// generic parameter types while preserving their serialized representation.
+#[cfg(feature = "serde")]
+impl<'de, T, D> Deserialize<'de> for DBSCANParameters<T, D>
+where
+    T: Number,
+    D: Distance<Vec<T>> + Deserialize<'de>,
+{
+    fn deserialize<De>(deserializer: De) -> Result<Self, De::Error>
+    where
+        De: serde::Deserializer<'de>,
+    {
+        #[derive(Deserialize)]
+        struct DBSCANParametersData<D> {
+            distance: D,
+            #[serde(default)]
+            min_samples: usize,
+            #[serde(default)]
+            eps: f64,
+            #[serde(default)]
+            algorithm: KNNAlgorithmName,
+            #[serde(default)]
+            _phantom_t: PhantomData<()>,
+        }
+
+        let data = DBSCANParametersData::deserialize(deserializer)?;
+
+        Ok(Self {
+            distance: data.distance,
+            min_samples: data.min_samples,
+            eps: data.eps,
+            algorithm: data.algorithm,
+            _phantom_t: PhantomData,
+        })
+    }
+}
+
 impl<T: Number, D: Distance<Vec<T>>> DBSCANParameters<T, D> {
     /// a function that defines a distance between each pair of point in training data.
     /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait.
@@ -295,7 +333,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
             x.row_iter()
                 .map(|row| row.iterator(0).cloned().collect())
                 .collect(),
-            parameters.distance,
+            parameters.distance.clone(),
         )?;
 
         let mut row = vec![TX::zero(); x.shape().1];
@@ -353,6 +391,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
             num_classes: k as usize,
             knn_algorithm: algo,
             eps: parameters.eps,
+            parameters: Some(parameters),
             _phantom_ty: PhantomData,
             _phantom_x: PhantomData,
             _phantom_y: PhantomData,
@@ -392,6 +431,14 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
 
         Ok(result)
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// `Some` with the parameters used to configure the model, or `None` if unavailable.
+    pub fn parameters(&self) -> Option<&DBSCANParameters<TX, D>> {
+        self.parameters.as_ref()
+    }
 }
 
 #[cfg(test)]
@@ -491,12 +538,34 @@ mod tests {
         ])
         .unwrap();
 
-        let dbscan = DBSCAN::fit(&x, Default::default()).unwrap();
+        let parameters = DBSCANParameters::default()
+            .with_eps(0.5)
+            .with_min_samples(2)
+            .with_algorithm(KNNAlgorithmName::LinearSearch);
+        let expected_parameters = parameters.clone();
+        let dbscan = DBSCAN::fit(&x, parameters).unwrap();
 
         let deserialized_dbscan: DBSCAN<f32, f32, DenseMatrix<f32>, Vec<f32>, Euclidian<f32>> =
             serde_json::from_str(&serde_json::to_string(&dbscan).unwrap()).unwrap();
 
         assert_eq!(dbscan, deserialized_dbscan);
+
+        let actual_parameters = deserialized_dbscan
+            .parameters()
+            .expect("parameters should survive serialization");
+        assert_eq!(
+            format!("{:?}", actual_parameters.distance),
+            format!("{:?}", expected_parameters.distance)
+        );
+        assert_eq!(
+            actual_parameters.min_samples,
+            expected_parameters.min_samples
+        );
+        assert_eq!(actual_parameters.eps, expected_parameters.eps);
+        assert_eq!(
+            std::mem::discriminant(&actual_parameters.algorithm),
+            std::mem::discriminant(&expected_parameters.algorithm)
+        );
     }
 
     #[cfg(feature = "datasets")]
@@ -514,4 +583,51 @@ mod tests {
 
         println!("{labels:?}");
     }
+
+    #[test]
+    fn test_can_get_assigned_parameters() {
+        let data = vec![0.0, 0.0, 0.5, 0.5, 10.0, 10.0];
+        let matrix = DenseMatrix::new(3, 2, data, false).unwrap();
+        let parameters: DBSCANParameters<f64, Euclidian<f64>> = DBSCANParameters::default()
+            .with_eps(1.0)
+            .with_min_samples(1);
+        let expected_parameters = parameters.clone();
+        let clustering = DBSCAN::<f64, f64, DenseMatrix<f64>, Vec<f64>, Euclidian<f64>>::fit(
+            &matrix, parameters,
+        )
+        .unwrap();
+
+        let actual_parameters = clustering
+            .parameters()
+            .expect("parameters should be set after fitting");
+        assert_eq!(
+            format!("{:?}", actual_parameters.distance),
+            format!("{:?}", expected_parameters.distance)
+        );
+        assert_eq!(
+            actual_parameters.min_samples,
+            expected_parameters.min_samples
+        );
+        assert_eq!(actual_parameters.eps, expected_parameters.eps);
+        assert_eq!(
+            std::mem::discriminant(&actual_parameters.algorithm),
+            std::mem::discriminant(&expected_parameters.algorithm)
+        );
+    }
+
+    #[test]
+    fn test_returns_none_on_no_parameters() {
+        let data = vec![0.0, 0.0, 0.5, 0.5, 10.0, 10.0];
+        let matrix = DenseMatrix::new(3, 2, data, false).unwrap();
+        let parameters: DBSCANParameters<f64, Euclidian<f64>> = DBSCANParameters::default()
+            .with_eps(1.0)
+            .with_min_samples(1);
+        let mut clustering = DBSCAN::<f64, f64, DenseMatrix<f64>, Vec<f64>, Euclidian<f64>>::fit(
+            &matrix, parameters,
+        )
+        .unwrap();
+        clustering.parameters = None;
+
+        assert!(clustering.parameters().is_none());
+    }
 }

src/cluster/kmeans.rs

@@ -76,6 +76,7 @@ pub struct KMeans<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> {
     size: Vec<usize>,
     _distortion: f64,
     centroids: Vec<Vec<f64>>,
+    parameters: Option<KMeansParameters>,
     _phantom_tx: PhantomData<TX>,
     _phantom_ty: PhantomData<TY>,
     _phantom_x: PhantomData<X>,
@@ -315,6 +316,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
             size,
             _distortion: distortion,
             centroids,
+            parameters: Some(parameters),
             _phantom_tx: PhantomData,
             _phantom_ty: PhantomData,
             _phantom_x: PhantomData,
@@ -411,6 +413,14 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
 
         y
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// `Some` with the parameters used to configure the model, or `None` if unavailable.
+    pub fn parameters(&self) -> Option<&KMeansParameters> {
+        self.parameters.as_ref()
+    }
 }
 
 #[cfg(test)]
@@ -543,4 +553,33 @@ mod tests {
 
         assert_eq!(kmeans, deserialized_kmeans);
     }
+
+    #[test]
+    fn test_can_get_assigned_parameters() {
+        let data = vec![0.0, 0.0, 5.0, 5.0, 10.0, 10.0];
+        let matrix = DenseMatrix::new(3, 2, data, false).unwrap();
+        let parameters = KMeansParameters::default().with_k(2);
+        let expected_parameters = parameters.clone();
+        let clustering =
+            KMeans::<f64, f64, DenseMatrix<f64>, Vec<f64>>::fit(&matrix, parameters).unwrap();
+
+        let actual_parameters = clustering
+            .parameters()
+            .expect("parameters should be set after fitting");
+        assert_eq!(actual_parameters.k, expected_parameters.k);
+        assert_eq!(actual_parameters.max_iter, expected_parameters.max_iter);
+        assert_eq!(actual_parameters.seed, expected_parameters.seed);
+    }
+
+    #[test]
+    fn test_returns_none_on_no_parameters() {
+        let data = vec![0.0, 0.0, 5.0, 5.0, 10.0, 10.0];
+        let matrix = DenseMatrix::new(3, 2, data, false).unwrap();
+        let parameters = KMeansParameters::default().with_k(2);
+        let mut clustering =
+            KMeans::<f64, f64, DenseMatrix<f64>, Vec<f64>>::fit(&matrix, parameters).unwrap();
+        clustering.parameters = None;
+
+        assert!(clustering.parameters().is_none());
+    }
 }

src/decomposition/pca.rs

@@ -65,6 +65,7 @@ pub struct PCA<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDe
     eigenvectors: X,
     eigenvalues: Vec<T>,
     projection: X,
+    parameters: Option<PCAParameters>,
     mu: Vec<T>,
     pmu: Vec<T>,
 }
@@ -329,6 +330,7 @@ impl<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable
             eigenvectors,
             eigenvalues,
             projection: projection.transpose(),
+            parameters: Some(parameters),
             mu,
             pmu,
         })
@@ -360,6 +362,14 @@ impl<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable
     pub fn components(&self) -> &X {
         &self.projection
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// `Some` with the parameters used to configure the model, or `None` if unavailable.
+    pub fn parameters(&self) -> Option<&PCAParameters> {
+        self.parameters.as_ref()
+    }
 }
 
 #[cfg(test)]
@@ -747,4 +757,36 @@ mod tests {
 
     //     assert_eq!(pca, deserialized_pca);
     // }
+
+    #[test]
+    fn test_can_get_assigned_parameters() {
+        let data = vec![0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0];
+        let matrix = DenseMatrix::new(4, 2, data, false).unwrap();
+        let parameters = PCAParameters::default().with_n_components(1);
+        let expected_parameters = parameters.clone();
+        let pca = PCA::<f64, DenseMatrix<f64>>::fit(&matrix, parameters).unwrap();
+
+        let actual_parameters = pca
+            .parameters()
+            .expect("parameters should be set after fitting");
+        assert_eq!(
+            actual_parameters.n_components,
+            expected_parameters.n_components
+        );
+        assert_eq!(
+            actual_parameters.use_correlation_matrix,
+            expected_parameters.use_correlation_matrix
+        );
+    }
+
+    #[test]
+    fn test_returns_none_on_no_parameters() {
+        let data = vec![0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0];
+        let matrix = DenseMatrix::new(4, 2, data, false).unwrap();
+        let parameters = PCAParameters::default().with_n_components(1);
+        let mut pca = PCA::<f64, DenseMatrix<f64>>::fit(&matrix, parameters).unwrap();
+        pca.parameters = None;
+
+        assert!(pca.parameters().is_none());
+    }
 }