Feature/generic ensemble

Cargo.toml

@@ -25,8 +25,8 @@ 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_distr = { version = "0.4", optional = true }
+rand = { version = "0.9", default-features = false, features = ["small_rng"] }
+rand_distr = { version = "0.5", optional = true }
 serde = { version = "1", features = ["derive"], optional = true }
 ordered-float = "5.1.0"
 
@@ -38,12 +38,12 @@ default = []
 serde = ["dep:serde", "dep:typetag"]
 ndarray-bindings = ["dep:ndarray"]
 datasets = ["dep:rand_distr", "std_rand", "serde"]
-std_rand = ["rand/std_rng", "rand/std"]
+std_rand = ["rand/std_rng", "rand/std", "rand/thread_rng"]
 # used by wasm32-unknown-unknown for in-browser usage
-js = ["getrandom/js"]
+js = ["getrandom/wasm_js"]
 
 [target.'cfg(target_arch = "wasm32")'.dependencies]
-getrandom = { version = "0.2.8", optional = true }
+getrandom = { version = "0.3", optional = true }
 
 [target.'cfg(all(target_arch = "wasm32", not(target_os = "wasi")))'.dev-dependencies]
 wasm-bindgen-test = "0.3"

src/cluster/kmeans.rs

@@ -356,7 +356,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
         let (n, _) = data.shape();
         let mut y = vec![0; n];
         let mut centroid: Vec<TX> = data
-            .get_row(rng.gen_range(0..n))
+            .get_row(rng.random_range(0..n))
             .iterator(0)
             .cloned()
             .collect();
@@ -382,7 +382,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>> KMeans<TX, TY, X, Y>
             for i in d.iter() {
                 sum += *i;
             }
-            let cutoff = rng.gen::<f64>() * sum;
+            let cutoff = rng.random::<f64>() * sum;
             let mut cost = 0f64;
             let mut index = 0;
             while index < n {

src/dataset/generator.rs

@@ -1,6 +1,6 @@
 //! # Dataset Generators
 //!
-use rand::distributions::Uniform;
+use rand::distr::Uniform;
 use rand::prelude::*;
 use rand_distr::Normal;
 
@@ -12,11 +12,11 @@ pub fn make_blobs(
     num_features: usize,
     num_centers: usize,
 ) -> Dataset<f32, f32> {
-    let center_box = Uniform::from(-10.0..10.0);
+    let center_box = Uniform::new(-10.0, 10.0).expect("Invalid uniform range");
     let cluster_std = 1.0;
     let mut centers: Vec<Vec<Normal<f32>>> = Vec::with_capacity(num_centers);
 
-    let mut rng = rand::thread_rng();
+    let mut rng = rand::rng();
     for _ in 0..num_centers {
         centers.push(
             (0..num_features)
@@ -60,7 +60,7 @@ pub fn make_circles(num_samples: usize, factor: f32, noise: f32) -> Dataset<f32,
     let linspace_in = linspace(0.0, 2.0 * std::f32::consts::PI, num_samples_in);
 
     let noise = Normal::new(0.0, noise).unwrap();
-    let mut rng = rand::thread_rng();
+    let mut rng = rand::rng();
 
     let mut x: Vec<f32> = Vec::with_capacity(num_samples * 2);
     let mut y: Vec<f32> = Vec::with_capacity(num_samples);
@@ -97,7 +97,7 @@ pub fn make_moons(num_samples: usize, noise: f32) -> Dataset<f32, u32> {
     let linspace_in = linspace(0.0, std::f32::consts::PI, num_samples_in);
 
     let noise = Normal::new(0.0, noise).unwrap();
-    let mut rng = rand::thread_rng();
+    let mut rng = rand::rng();
 
     let mut x: Vec<f32> = Vec::with_capacity(num_samples * 2);
     let mut y: Vec<f32> = Vec::with_capacity(num_samples);

src/ensemble/base_forest_regressor.rs

@@ -212,7 +212,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
     fn sample_with_replacement(nrows: usize, rng: &mut impl Rng) -> Vec<usize> {
         let mut samples = vec![0; nrows];
         for _ in 0..nrows {
-            let xi = rng.gen_range(0..nrows);
+            let xi = rng.random_range(0..nrows);
             samples[xi] += 1;
         }
         samples