Release 0.3 (#235)

src/svm/svc.rs

@@ -20,7 +20,7 @@
 //!
 //! Where \\( m \\) is a number of training samples, \\( y_i \\) is a label value (either 1 or -1) and \\(\langle\vec{w}, \vec{x}_i \rangle + b\\) is a decision boundary.
 //!
-//! To solve this optimization problem, SmartCore uses an [approximate SVM solver](https://leon.bottou.org/projects/lasvm).
+//! To solve this optimization problem, `smartcore` uses an [approximate SVM solver](https://leon.bottou.org/projects/lasvm).
 //! The optimizer reaches accuracies similar to that of a real SVM after performing two passes through the training examples. You can choose the number of passes
 //! through the data that the algorithm takes by changing the `epoch` parameter of the classifier.
 //!
@@ -934,8 +934,7 @@ mod tests {
     use super::*;
     use crate::linalg::basic::matrix::DenseMatrix;
     use crate::metrics::accuracy;
-    #[cfg(feature = "serde")]
-    use crate::svm::*;
+    use crate::svm::Kernels;
 
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),