feat: Make SerDe optional

src/linear/ridge_regression.rs

@@ -58,7 +58,7 @@
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 use std::fmt::Debug;
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
@@ -66,7 +66,8 @@ use crate::linalg::BaseVector;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 /// Approach to use for estimation of regression coefficients. Cholesky is more efficient but SVD is more stable.
 pub enum RidgeRegressionSolverName {
     /// Cholesky decomposition, see [Cholesky](../../linalg/cholesky/index.html)
@@ -76,7 +77,8 @@ pub enum RidgeRegressionSolverName {
 }
 
 /// Ridge Regression parameters
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct RidgeRegressionParameters<T: RealNumber> {
     /// Solver to use for estimation of regression coefficients.
     pub solver: RidgeRegressionSolverName,
@@ -88,7 +90,8 @@ pub struct RidgeRegressionParameters<T: RealNumber> {
 }
 
 /// Ridge regression
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct RidgeRegression<T: RealNumber, M: Matrix<T>> {
     coefficients: M,
     intercept: T,