Derive clone for NB Parameters

src/naive_bayes/bernoulli.rs

@@ -76,7 +76,7 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for BernoulliNBDistributi
 }
 
 /// `BernoulliNB` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug)]
+#[derive(Serialize, Deserialize, Debug, Clone)]
 pub struct BernoulliNBParameters<T: RealNumber> {
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: T,

src/naive_bayes/categorical.rs

@@ -215,7 +215,7 @@ impl<T: RealNumber> CategoricalNBDistribution<T> {
 }
 
 /// `CategoricalNB` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug)]
+#[derive(Serialize, Deserialize, Debug, Clone)]
 pub struct CategoricalNBParameters<T: RealNumber> {
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: T,

src/naive_bayes/gaussian.rs

@@ -74,7 +74,7 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for GaussianNBDistributio
 }
 
 /// `GaussianNB` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug, Default)]
+#[derive(Serialize, Deserialize, Debug, Default, Clone)]
 pub struct GaussianNBParameters<T: RealNumber> {
     /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
     pub priors: Option<Vec<T>>,

src/naive_bayes/multinomial.rs

@@ -72,7 +72,7 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for MultinomialNBDistribu
 }
 
 /// `MultinomialNB` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug)]
+#[derive(Serialize, Deserialize, Debug, Clone)]
 pub struct MultinomialNBParameters<T: RealNumber> {
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: T,