Fix feature management and README.

Use references where possible.

Author: LukeMathWalker

README.md

@@ -2,6 +2,8 @@
 
 A collection of examples leveraging the `ndarray` ecosystem.
 
+Each example folder contains a description and instructions on how to run it. Do not run `cargo run` or `cargo build` from the top level folder!
+
 Table of contents:
 
 - [Linear regression](https://github.com/rust-ndarray/ndarray-examples/tree/master/linear_regression)

linear_regression/Cargo.toml

@@ -5,9 +5,15 @@ authors = ["LukeMathWalker"]
 edition = "2018"
 workspace = ".."
 
+[features]
+default = []
+openblas = ["ndarray-linalg/openblas"]
+intel-mkl = ["ndarray-linalg/intel-mkl"]
+netlib = ["ndarray-linalg/netlib"]
+
 [dependencies]
 ndarray = {version = "0.12", features = ["blas"]}
-ndarray-linalg = {version = "0.11.1", features = ["openblas"]}
+ndarray-linalg = {version = "0.11.1", optional = true, default-features = false}
 ndarray-stats = {git = "https://github.com/rust-ndarray/ndarray-stats", branch = "master"}
 ndarray-rand = "0.9"
 rand = "0.6"

linear_regression/src/lib.rs

@@ -36,7 +36,7 @@ impl LinearRegression {
     /// to match the training data distribution.
     ///
     /// `self` is modified in place, nothing is returned.
-    pub fn fit<A, B>(&mut self, X: ArrayBase<A, Ix2>, y: ArrayBase<B, Ix1>)
+    pub fn fit<A, B>(&mut self, X: &ArrayBase<A, Ix2>, y: &ArrayBase<B, Ix1>)
     where
         A: Data<Elem = f64>,
         B: Data<Elem = f64>,
@@ -50,7 +50,7 @@ impl LinearRegression {
         self.beta = if self.fit_intercept {
             let dummy_column: Array<f64, _> = Array::ones((n_samples, 1));
             let X = stack(Axis(1), &[dummy_column.view(), X.view()]).unwrap();
-            Some(LinearRegression::solve_normal_equation(X, y))
+            Some(LinearRegression::solve_normal_equation(&X, y))
         } else {
             Some(LinearRegression::solve_normal_equation(X, y))
         };
@@ -77,13 +77,13 @@ impl LinearRegression {
         }
     }
 
-    fn solve_normal_equation<A, B>(X: ArrayBase<A, Ix2>, y: ArrayBase<B, Ix1>) -> Array1<f64>
+    fn solve_normal_equation<A, B>(X: &ArrayBase<A, Ix2>, y: &ArrayBase<B, Ix1>) -> Array1<f64>
     where
         A: Data<Elem = f64>,
         B: Data<Elem = f64>,
     {
-        let rhs = X.t().dot(&y);
-        let linear_operator = X.t().dot(&X);
+        let rhs = X.t().dot(y);
+        let linear_operator = X.t().dot(X);
         linear_operator.solve_into(rhs).unwrap()
     }
 

linear_regression/src/main.rs

@@ -33,7 +33,7 @@ pub fn main() {
     let (y_train, y_test) = y.view().split_at(Axis(0), n_train_samples);
 
     let mut linear_regressor = LinearRegression::new(false);
-    linear_regressor.fit(X_train, y_train);
+    linear_regressor.fit(&X_train, &y_train);
 
     let test_predictions = linear_regressor.predict(&X_test);
     let mean_squared_error = test_predictions.mean_sq_err(&y_test.to_owned()).unwrap();