prepare for release

Cargo.toml

@@ -2,7 +2,7 @@
 name = "smartcore"
 description = "Machine Learning in Rust."
 homepage = "https://smartcorelib.org"
-version = "0.4.9"
+version = "0.4.8"
 authors = ["smartcore Developers"]
 edition = "2021"
 license = "Apache-2.0"

src/linear/lasso.rs

@@ -166,7 +166,7 @@ pub struct LassoSearchParameters {
     /// The maximum number of iterations
     pub max_iter: Vec<usize>,
     #[cfg_attr(feature = "serde", serde(default))]
-    /// If false, force the intercept parameter (beta_0) to be zero.
+    /// The maximum number of iterations
     pub fit_intercept: Vec<bool>,
 }
 

src/linear/lasso_optimizer.rs

@@ -53,7 +53,6 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
         let lambda = lambda.max(T::epsilon());
 
         //parameters
-        let max_ls_iter = 100;
         let pcgmaxi = 5000;
         let min_pcgtol = T::from_f64(0.1).unwrap();
         let eta = T::from_f64(1E-3).unwrap();
@@ -69,6 +68,7 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
             y.to_owned()
         };
 
+        let mut max_ls_iter = 100;
         let mut pitr = 0;
         let mut w = Vec::zeros(p);
         let mut neww = w.clone();
@@ -170,7 +170,7 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
             s = T::one();
             let gdx = grad.dot(&dxu);
 
-            let mut lsiter = 0;
+            let lsiter = 0;
             while lsiter < max_ls_iter {
                 for i in 0..p {
                     neww[i] = w[i] + s * dx[i];
@@ -195,7 +195,7 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
                     }
                 }
                 s = beta * s;
-                lsiter += 1;
+                max_ls_iter += 1;
             }
 
             if lsiter == max_ls_iter {