Update rand requirement from 0.8.5 to 0.9.2

Updates the requirements on [rand](https://github.com/rust-random/rand) to permit the latest version.
- [Release notes](https://github.com/rust-random/rand/releases)
- [Changelog](https://github.com/rust-random/rand/blob/master/CHANGELOG.md)
- [Commits](https://github.com/rust-random/rand/compare/0.8.5...rand_core-0.9.2)

---
updated-dependencies:
- dependency-name: rand
  dependency-version: 0.9.2
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

CHANGELOG.md

@@ -4,7 +4,7 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [Unreleased]
+## [0.4.8] - 2025-11-29
 - WARNING: Breaking changes!
 - `LassoParameters` and `LassoSearchParameters` have a new field `fit_intercept`. When it is set to false, the `beta_0` term in the formula will be forced to zero, and `intercept` field in `Lasso` will be set to `None`.
 

Cargo.toml

@@ -2,7 +2,7 @@
 name = "smartcore"
 description = "Machine Learning in Rust."
 homepage = "https://smartcorelib.org"
-version = "0.4.7"
+version = "0.4.9"
 authors = ["smartcore Developers"]
 edition = "2021"
 license = "Apache-2.0"
@@ -25,7 +25,7 @@ cfg-if = "1.0.0"
 ndarray = { version = "0.15", optional = true }
 num-traits = "0.2.12"
 num = "0.4"
-rand = { version = "0.8.5", default-features = false, features = ["small_rng"] }
+rand = { version = "0.9.2", default-features = false, features = ["small_rng"] }
 rand_distr = { version = "0.4", optional = true }
 serde = { version = "1", features = ["derive"], optional = true }
 ordered-float = "5.1.0"

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))]
-    /// The maximum number of iterations
+    /// If false, force the intercept parameter (beta_0) to be zero.
     pub fit_intercept: Vec<bool>,
 }
 

src/linear/lasso_optimizer.rs

@@ -53,6 +53,7 @@ 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();
@@ -68,7 +69,6 @@ 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 lsiter = 0;
+            let mut 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;
-                max_ls_iter += 1;
+                lsiter += 1;
             }
 
             if lsiter == max_ls_iter {