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 +177,22 @@ 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
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &AgglomerativeClusteringParameters {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>>

src/cluster/dbscan.rs

@@ -64,6 +64,7 @@ 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>,
@@ -295,7 +296,7 @@ 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 +354,7 @@ 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 +394,15 @@ 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
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &DBSCANParameters<TX, D> {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 #[cfg(test)]

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 +413,15 @@ 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
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &KMeansParameters {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 #[cfg(test)]

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 +362,15 @@ 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
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &PCAParameters {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 #[cfg(test)]

src/decomposition/svd.rs

@@ -62,6 +62,7 @@ use crate::numbers::realnum::RealNumber;
 #[derive(Debug)]
 pub struct SVD<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable<T>> {
     components: X,
+    parameters: Option<SVDParameters>,
     phantom: PhantomData<T>,
 }
 
@@ -190,6 +191,7 @@ impl<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable
 
         Ok(SVD {
             components,
+            parameters: Some(parameters),
             phantom: PhantomData,
         })
     }
@@ -212,6 +214,15 @@ impl<T: Number + RealNumber, X: Array2<T> + SVDDecomposable<T> + EVDDecomposable
     pub fn components(&self) -> &X {
         &self.components
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &SVDParameters {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 #[cfg(test)]

src/ensemble/extra_trees_regressor.rs

@@ -104,6 +104,7 @@ pub struct ExtraTreesRegressor<
     Y: Array1<TY>,
 > {
     forest_regressor: Option<BaseForestRegressor<TX, TY, X, Y>>,
+    parameters: Option<ExtraTreesRegressorParameters>
 }
 
 impl ExtraTreesRegressorParameters {
@@ -165,6 +166,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
     fn new() -> Self {
         Self {
             forest_regressor: Option::None,
+            parameters: Option::None
         }
     }
 
@@ -207,6 +209,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
 
         Ok(ExtraTreesRegressor {
             forest_regressor: Some(forest_regressor),
+            parameters: Some(parameters)
         })
     }
 
@@ -222,6 +225,15 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
         let forest_regressor = self.forest_regressor.as_ref().unwrap();
         forest_regressor.predict_oob(x)
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &ExtraTreesRegressorParameters {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 #[cfg(test)]

src/ensemble/random_forest_classifier.rs

@@ -107,6 +107,7 @@ pub struct RandomForestClassifier<
     trees: Option<Vec<DecisionTreeClassifier<TX, TY, X, Y>>>,
     classes: Option<Vec<TY>>,
     samples: Option<Vec<Vec<bool>>>,
+    parameters: Option<RandomForestClassifierParameters>
 }
 
 impl RandomForestClassifierParameters {
@@ -200,6 +201,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y:
             trees: Option::None,
             classes: Option::None,
             samples: Option::None,
+            parameters: Option::None,
         }
     }
     fn fit(x: &X, y: &Y, parameters: RandomForestClassifierParameters) -> Result<Self, Failed> {
@@ -506,6 +508,7 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
             trees: Some(trees),
             classes: Some(classes),
             samples: maybe_all_samples,
+            parameters: Some(parameters)
         })
     }
 
@@ -613,6 +616,15 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
         }
         samples
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &RandomForestClassifierParameters {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 #[cfg(test)]

src/ensemble/random_forest_regressor.rs

@@ -95,6 +95,7 @@ pub struct RandomForestRegressor<
     Y: Array1<TY>,
 > {
     forest_regressor: Option<BaseForestRegressor<TX, TY, X, Y>>,
+    parameters: Option<RandomForestRegressorParameters>
 }
 
 impl RandomForestRegressorParameters {
@@ -165,6 +166,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
     fn new() -> Self {
         Self {
             forest_regressor: Option::None,
+            parameters: Option::None,
         }
     }
 
@@ -399,6 +401,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
 
         Ok(RandomForestRegressor {
             forest_regressor: Some(forest_regressor),
+            parameters: Some(parameters)
         })
     }
 
@@ -414,6 +417,15 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
         let forest_regressor = self.forest_regressor.as_ref().unwrap();
         forest_regressor.predict_oob(x)
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &RandomForestRegressorParameters {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 #[cfg(test)]

src/linear/elastic_net.rs

@@ -98,6 +98,7 @@ pub struct ElasticNetParameters {
 pub struct ElasticNet<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> {
     coefficients: Option<X>,
     intercept: Option<TX>,
+    parameters: Option<ElasticNetParameters>,
     _phantom_ty: PhantomData<TY>,
     _phantom_y: PhantomData<Y>,
 }
@@ -288,6 +289,7 @@ impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>>
         Self {
             coefficients: Option::None,
             intercept: Option::None,
+            parameters: Option::None,
             _phantom_ty: PhantomData,
             _phantom_y: PhantomData,
         }
@@ -385,6 +387,7 @@ impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>>
         Ok(ElasticNet {
             intercept: Some(b),
             coefficients: Some(w),
+            parameters: Some(parameters),
             _phantom_ty: PhantomData,
             _phantom_y: PhantomData,
         })
@@ -459,6 +462,15 @@ impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>>
 
         (x2, y2, gamma)
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &ElasticNetParameters {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 #[cfg(test)]

src/linear/lasso.rs

@@ -64,6 +64,7 @@ pub struct LassoParameters {
 pub struct Lasso<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> {
     coefficients: Option<X>,
     intercept: Option<TX>,
+    parameters: Option<LassoParameters>,
     _phantom_ty: PhantomData<TY>,
     _phantom_y: PhantomData<Y>,
 }
@@ -130,6 +131,7 @@ impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>>
         Self {
             coefficients: None,
             intercept: None,
+            parameters: None,
             _phantom_ty: PhantomData,
             _phantom_y: PhantomData,
         }
@@ -352,6 +354,7 @@ impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> Las
         Ok(Lasso {
             intercept: b,
             coefficients: Some(w),
+            parameters: Some(parameters),
             _phantom_ty: PhantomData,
             _phantom_y: PhantomData,
         })
@@ -402,6 +405,15 @@ impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> Las
         scaled_x.scale_mut(&col_mean, &col_std, 0);
         Ok((scaled_x, col_mean, col_std))
     }
+
+    /// Getter for parameters used in the model
+    ///
+    /// # Returns
+    /// Parameters used to setup the model
+    pub fn parameters(&self) -> &LassoParameters {
+        assert!(self.parameters.is_some());
+        &self.parameters.as_ref().unwrap()
+    }
 }
 
 #[cfg(test)]