Merge branch 'development' into feature/generic_ensemble

Author: skywardfire1

Cargo.toml

@@ -25,8 +25,7 @@ cfg-if = "1.0.0"
 ndarray = { version = "0.15", optional = true }
 num-traits = "0.2.12"
 num = "0.4"
-rand = { version = "0.9", default-features = false, features = ["small_rng"] }
-rand_distr = { version = "0.5", optional = true }
+rand = { version = "0.10.1", default-features = false, features = ["alloc"] }
 serde = { version = "1", features = ["derive"], optional = true }
 ordered-float = "5.1.0"
 
@@ -37,13 +36,13 @@ typetag = { version = "0.2", optional = true }
 default = []
 serde = ["dep:serde", "dep:typetag"]
 ndarray-bindings = ["dep:ndarray"]
-datasets = ["dep:rand_distr", "std_rand", "serde"]
+datasets = ["std_rand", "serde"]
 std_rand = ["rand/std_rng", "rand/std", "rand/thread_rng"]
 # used by wasm32-unknown-unknown for in-browser usage
 js = ["getrandom/wasm_js"]
 
 [target.'cfg(target_arch = "wasm32")'.dependencies]
-getrandom = { version = "0.3", optional = true }
+getrandom = { version = "0.4", optional = true }
 
 [target.'cfg(all(target_arch = "wasm32", not(target_os = "wasi")))'.dev-dependencies]
 wasm-bindgen-test = "0.3"

src/cluster/kmeans.rs

@@ -55,7 +55,7 @@
 use std::fmt::Debug;
 use std::marker::PhantomData;
 
-use rand::Rng;
+use rand::RngExt;
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 

src/dataset/generator.rs

@@ -1,38 +1,47 @@
 //! # Dataset Generators
 //!
+use rand::distr::Distribution;
 use rand::distr::Uniform;
-use rand::prelude::*;
-use rand_distr::Normal;
 
 use crate::dataset::Dataset;
 
+/// Sample from N(mean, std) via Box-Muller transform using only rand 0.10
+#[inline]
+fn sample_normal(mean: f32, std: f32, rng: &mut impl rand::Rng) -> f32 {
+    let unit = Uniform::new(f32::EPSILON, 1.0f32).unwrap();
+    let u1 = unit.sample(rng);
+    let u2 = unit.sample(rng);
+    let z = (-2.0 * u1.ln()).sqrt() * (2.0 * std::f32::consts::PI * u2).cos();
+    mean + std * z
+}
+
 /// Generate `num_centers` clusters of normally distributed points
 pub fn make_blobs(
     num_samples: usize,
     num_features: usize,
     num_centers: usize,
 ) -> Dataset<f32, f32> {
-    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 center_box = Uniform::new(-10.0f32, 10.0f32).expect("Invalid uniform range");
+    let cluster_std = 1.0f32;
     let mut rng = rand::rng();
-    for _ in 0..num_centers {
-        centers.push(
+
+    // Pre-compute cluster centers (one mean per feature per cluster)
+    let centers: Vec<Vec<f32>> = (0..num_centers)
+        .map(|_| {
             (0..num_features)
-                .map(|_| Normal::new(center_box.sample(&mut rng), cluster_std).unwrap())
-                .collect(),
-        );
-    }
+                .map(|_| center_box.sample(&mut rng))
+                .collect()
+        })
+        .collect();
 
     let mut y: Vec<f32> = Vec::with_capacity(num_samples);
-    let mut x: Vec<f32> = Vec::with_capacity(num_samples);
+    let mut x: Vec<f32> = Vec::with_capacity(num_samples * num_features);
 
     for i in 0..num_samples {
         let label = i % num_centers;
         y.push(label as f32);
         for j in 0..num_features {
-            x.push(centers[label][j].sample(&mut rng));
+            x.push(sample_normal(centers[label][j], cluster_std, &mut rng));
         }
     }
 
@@ -59,21 +68,20 @@ pub fn make_circles(num_samples: usize, factor: f32, noise: f32) -> Dataset<f32,
     let linspace_out = linspace(0.0, 2.0 * std::f32::consts::PI, num_samples_out);
     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::rng();
 
     let mut x: Vec<f32> = Vec::with_capacity(num_samples * 2);
     let mut y: Vec<f32> = Vec::with_capacity(num_samples);
 
     for v in linspace_out {
-        x.push(v.cos() + noise.sample(&mut rng));
-        x.push(v.sin() + noise.sample(&mut rng));
+        x.push(v.cos() + sample_normal(0.0, noise, &mut rng));
+        x.push(v.sin() + sample_normal(0.0, noise, &mut rng));
         y.push(0.0);
     }
 
     for v in linspace_in {
-        x.push(v.cos() * factor + noise.sample(&mut rng));
-        x.push(v.sin() * factor + noise.sample(&mut rng));
+        x.push(v.cos() * factor + sample_normal(0.0, noise, &mut rng));
+        x.push(v.sin() * factor + sample_normal(0.0, noise, &mut rng));
         y.push(1.0);
     }
 
@@ -96,21 +104,20 @@ pub fn make_moons(num_samples: usize, noise: f32) -> Dataset<f32, u32> {
     let linspace_out = linspace(0.0, std::f32::consts::PI, num_samples_out);
     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::rng();
 
     let mut x: Vec<f32> = Vec::with_capacity(num_samples * 2);
     let mut y: Vec<f32> = Vec::with_capacity(num_samples);
 
     for v in linspace_out {
-        x.push(v.cos() + noise.sample(&mut rng));
-        x.push(v.sin() + noise.sample(&mut rng));
+        x.push(v.cos() + sample_normal(0.0, noise, &mut rng));
+        x.push(v.sin() + sample_normal(0.0, noise, &mut rng));
         y.push(0.0);
     }
 
     for v in linspace_in {
-        x.push(1.0 - v.cos() + noise.sample(&mut rng));
-        x.push(1.0 - v.sin() + noise.sample(&mut rng) - 0.5);
+        x.push(1.0 - v.cos() + sample_normal(0.0, noise, &mut rng));
+        x.push(1.0 - v.sin() + sample_normal(0.0, noise, &mut rng) - 0.5);
         y.push(1.0);
     }
 

src/ensemble/base_forest_regressor.rs

@@ -1,4 +1,4 @@
-use rand::Rng;
+use rand::RngExt;
 use std::fmt::Debug;
 
 #[cfg(feature = "serde")]
@@ -209,7 +209,7 @@ impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1
         result / TY::from(n_trees).unwrap()
     }
 
-    fn sample_with_replacement(nrows: usize, rng: &mut impl Rng) -> Vec<usize> {
+    fn sample_with_replacement(nrows: usize, rng: &mut impl rand::Rng) -> Vec<usize> {
         let mut samples = vec![0; nrows];
         for _ in 0..nrows {
             let xi = rng.random_range(0..nrows);

src/ensemble/random_forest_classifier.rs

@@ -45,7 +45,7 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-use rand::Rng;
+use rand::RngExt;
 
 use std::default::Default;
 use std::fmt::Debug;
@@ -587,7 +587,11 @@ impl<TX: FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY
         which_max(&result)
     }
 
-    fn sample_with_replacement(y: &[usize], num_classes: usize, rng: &mut impl Rng) -> Vec<usize> {
+    fn sample_with_replacement(
+        y: &[usize],
+        num_classes: usize,
+        rng: &mut impl rand::Rng,
+    ) -> Vec<usize> {
         let class_weight = vec![1.; num_classes];
         let nrows = y.len();
         let mut samples = vec![0; nrows];

src/numbers/floatnum.rs

@@ -56,7 +56,7 @@ impl FloatNumber for f64 {
     }
 
     fn rand() -> f64 {
-        use rand::Rng;
+        use rand::RngExt;
         let mut rng = get_rng_impl(None);
         rng.random()
     }
@@ -98,7 +98,7 @@ impl FloatNumber for f32 {
     }
 
     fn rand() -> f32 {
-        use rand::Rng;
+        use rand::RngExt;
         let mut rng = get_rng_impl(None);
         rng.random()
     }

src/numbers/realnum.rs

@@ -3,7 +3,7 @@
 //! This module defines real number and some useful functions that are used in [Linear Algebra](../../linalg/index.html) module.
 
 use rand::rngs::SmallRng;
-use rand::{Rng, SeedableRng};
+use rand::{RngExt, SeedableRng};
 
 use num_traits::Float;
 

src/rand_custom.rs

@@ -11,8 +11,10 @@ pub fn get_rng_impl(seed: Option<u64>) -> RngImpl {
         None => {
             cfg_if::cfg_if! {
                 if #[cfg(feature = "std_rand")] {
-                    use rand::RngCore;
+                    use rand::Rng;
                     // FIX: thread_rng() deprecated in rand 0.9 → use rng()
+                    // FIX: rand 0.10 no longer re-exports RngCore at root;
+                    //      import rand::Rng (supertrait) instead so next_u64() resolves
                     RngImpl::seed_from_u64(rand::rng().next_u64())
                 } else {
                     // no std_random feature build, use getrandom

src/tree/base_tree_regressor.rs

@@ -4,7 +4,7 @@ use std::fmt::Debug;
 use std::marker::PhantomData;
 
 use rand::seq::SliceRandom;
-use rand::Rng;
+use rand::RngExt;
 
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
@@ -292,7 +292,7 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
         &mut self,
         visitor: &mut NodeVisitor<'_, TX, TY, X, Y>,
         mtry: usize,
-        rng: &mut impl Rng,
+        rng: &mut impl rand::Rng,
     ) -> bool {
         let (_, n_attr) = visitor.x.shape();
 
@@ -336,7 +336,7 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
         sum: f64,
         parent_gain: f64,
         j: usize,
-        rng: &mut impl Rng,
+        rng: &mut impl rand::Rng,
     ) {
         let (min_val, max_val) = {
             let mut min_opt = None;
@@ -476,7 +476,7 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
         mut visitor: NodeVisitor<'a, TX, TY, X, Y>,
         mtry: usize,
         visitor_queue: &mut LinkedList<NodeVisitor<'a, TX, TY, X, Y>>,
-        rng: &mut impl Rng,
+        rng: &mut impl rand::Rng,
     ) -> bool {
         let (n, _) = visitor.x.shape();
         let mut tc = 0;