Release 0.3 (#235)

src/svm/mod.rs

@@ -9,7 +9,7 @@
 //! SVM is memory efficient since it uses only a subset of training data to find a decision boundary. This subset is called support vectors.
 //!
 //! In SVM distance between a data point and the support vectors is defined by the kernel function.
-//! SmartCore supports multiple kernel functions but you can always define a new kernel function by implementing the `Kernel` trait. Not all functions can be a kernel.
+//! `smartcore` supports multiple kernel functions but you can always define a new kernel function by implementing the `Kernel` trait. Not all functions can be a kernel.
 //! Building a new kernel requires a good mathematical understanding of the [Mercer theorem](https://en.wikipedia.org/wiki/Mercer%27s_theorem)
 //! that gives necessary and sufficient condition for a function to be a kernel function.
 //!

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")),

src/svm/svr.rs

@@ -596,7 +596,6 @@ mod tests {
     use super::*;
     use crate::linalg::basic::matrix::DenseMatrix;
     use crate::metrics::mean_squared_error;
-    #[cfg(feature = "serde")]
     use crate::svm::Kernels;
 
     // #[test]
@@ -617,7 +616,6 @@ mod tests {
     //     assert!(iter.next().is_none());
     // }
 
-    //TODO: had to disable this test as it runs for too long
     #[cfg_attr(
         all(target_arch = "wasm32", not(target_os = "wasi")),
         wasm_bindgen_test::wasm_bindgen_test