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,
         })

src/linear/logistic_regression.rs

@@ -5,7 +5,7 @@
 //!
 //! \\[ Pr(y=1) \approx \frac{e^{\beta_0 + \sum_{i=1}^n \beta_iX_i}}{1 + e^{\beta_0 + \sum_{i=1}^n \beta_iX_i}} \\]
 //!
-//! SmartCore uses [limited memory BFGS](https://en.wikipedia.org/wiki/Limited-memory_BFGS) method to find estimates of regression coefficients, \\(\beta\\)
+//! `smartcore` uses [limited memory BFGS](https://en.wikipedia.org/wiki/Limited-memory_BFGS) method to find estimates of regression coefficients, \\(\beta\\)
 //!
 //! Example:
 //!

src/linear/ridge_regression.rs

@@ -12,7 +12,7 @@
 //! where \\(\alpha \geq 0\\) is a tuning parameter that controls strength of regularization. When \\(\alpha = 0\\) the penalty term has no effect, and ridge regression will produce the least squares estimates.
 //! However, as \\(\alpha \rightarrow \infty\\), the impact of the shrinkage penalty grows, and the ridge regression coefficient estimates will approach zero.
 //!
-//! SmartCore uses [SVD](../../linalg/svd/index.html) and [Cholesky](../../linalg/cholesky/index.html) matrix decomposition to find estimates of \\(\hat{\beta}\\).
+//! `smartcore` uses [SVD](../../linalg/svd/index.html) and [Cholesky](../../linalg/cholesky/index.html) matrix decomposition to find estimates of \\(\hat{\beta}\\).
 //! The Cholesky 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 Cholesky decomposition, all matrices have an SVD decomposition.
 //!
@@ -197,7 +197,6 @@ pub struct RidgeRegression<
 > {
     coefficients: Option<X>,
     intercept: Option<TX>,
-    solver: Option<RidgeRegressionSolverName>,
     _phantom_ty: PhantomData<TY>,
     _phantom_y: PhantomData<Y>,
 }
@@ -259,7 +258,6 @@ impl<
         Self {
             coefficients: Option::None,
             intercept: Option::None,
-            solver: Option::None,
             _phantom_ty: PhantomData,
             _phantom_y: PhantomData,
         }
@@ -367,7 +365,6 @@ impl<
         Ok(RidgeRegression {
             intercept: Some(b),
             coefficients: Some(w),
-            solver: Some(parameters.solver),
             _phantom_ty: PhantomData,
             _phantom_y: PhantomData,
         })