feat: lasso documentation

src/linear/lasso.rs

@@ -105,18 +105,15 @@ impl<T: RealNumber, M: Matrix<T>> Lasso<T, M> {
             return Err(Failed::fit("Number of rows in X should = len(y)"));
         }
 
+        let l1_reg = parameters.alpha * T::from_usize(n).unwrap();
+
         let (w, b) = if parameters.normalize {
             let (scaled_x, col_mean, col_std) = Self::rescale_x(x)?;
 
             let mut optimizer = InteriorPointOptimizer::new(&scaled_x, p);
 
-            let mut w = optimizer.optimize(
-                &scaled_x,
-                y,
-                parameters.alpha,
-                parameters.max_iter,
-                parameters.tol,
-            )?;
+            let mut w =
+                optimizer.optimize(&scaled_x, y, l1_reg, parameters.max_iter, parameters.tol)?;
 
             for (j, col_std_j) in col_std.iter().enumerate().take(p) {
                 w.set(j, 0, w.get(j, 0) / *col_std_j);
@@ -133,8 +130,7 @@ impl<T: RealNumber, M: Matrix<T>> Lasso<T, M> {
         } else {
             let mut optimizer = InteriorPointOptimizer::new(x, p);
 
-            let w =
-                optimizer.optimize(x, y, parameters.alpha, parameters.max_iter, parameters.tol)?;
+            let w = optimizer.optimize(x, y, l1_reg, parameters.max_iter, parameters.tol)?;
 
             (w, y.mean())
         };
@@ -215,18 +211,9 @@ mod tests {
             114.2, 115.7, 116.9,
         ];
 
-        let y_hat = Lasso::fit(
-            &x,
-            &y,
-            LassoParameters {
-                alpha: 0.1,
-                normalize: true,
-                tol: 1e-4,
-                max_iter: 1000,
-            },
-        )
-        .and_then(|lr| lr.predict(&x))
-        .unwrap();
+        let y_hat = Lasso::fit(&x, &y, Default::default())
+            .and_then(|lr| lr.predict(&x))
+            .unwrap();
 
         assert!(mean_absolute_error(&y_hat, &y) < 2.0);