Release 0.3 (#235)

src/linear/linear_regression.rs

@@ -12,7 +12,7 @@
 //! \\[\hat{\beta} = (X^TX)^{-1}X^Ty \\]
 //!
 //! the \\((X^TX)^{-1}\\) term is both computationally expensive and numerically unstable. An alternative approach is to use a matrix decomposition to avoid this operation.
-//! SmartCore uses [SVD](../../linalg/svd/index.html) and [QR](../../linalg/qr/index.html) matrix decomposition to find estimates of \\(\hat{\beta}\\).
+//! `smartcore` uses [SVD](../../linalg/svd/index.html) and [QR](../../linalg/qr/index.html) matrix decomposition to find estimates of \\(\hat{\beta}\\).
 //! The QR decomposition is more computationally efficient and more numerically stable than calculating the normal equation directly,
 //! but does not work for all data matrices. Unlike the QR decomposition, all matrices have an SVD decomposition.
 //!
@@ -113,7 +113,6 @@ pub struct LinearRegression<
 > {
     coefficients: Option<X>,
     intercept: Option<TX>,
-    solver: LinearRegressionSolverName,
     _phantom_ty: PhantomData<TY>,
     _phantom_y: PhantomData<Y>,
 }
@@ -210,7 +209,6 @@ impl<
         Self {
             coefficients: Option::None,
             intercept: Option::None,
-            solver: LinearRegressionParameters::default().solver,
             _phantom_ty: PhantomData,
             _phantom_y: PhantomData,
         }
@@ -276,7 +274,6 @@ impl<
         Ok(LinearRegression {
             intercept: Some(*w.get((num_attributes, 0))),
             coefficients: Some(weights),
-            solver: parameters.solver,
             _phantom_ty: PhantomData,
             _phantom_y: PhantomData,
         })