migrate to rand 0.9.0

Cargo.toml

@@ -14,7 +14,7 @@ repository = "https://github.com/jonhoo/rust-zipf"
 documentation = "https://docs.rs/zipf"
 
 [dependencies]
-rand = { version = "0.8.0", default-features = false }
+rand = { version = "0.9.0", default-features = false }
 
 [dev-dependencies]
-rand = "0.8.0"
+rand = "0.9.0"

benches/zipf_bench.rs

@@ -8,16 +8,16 @@ use test::Bencher;
 #[bench]
 fn bench_us(b: &mut Bencher) {
     use rand;
-    use rand::distributions::Distribution;
+    use rand::distr::Distribution;
     use zipf::ZipfDistribution;
-    let mut rng = rand::thread_rng();
+    let mut rng = rand::rng();
     let us = ZipfDistribution::new(1000000, 1.07).unwrap();
     b.iter(|| us.sample(&mut rng));
 }
 
 #[bench]
 fn bench_threadrng(b: &mut Bencher) {
     use rand::{self, Rng};
-    let mut rng = rand::thread_rng();
-    b.iter(|| rng.gen::<u64>());
+    let mut rng = rand::rng();
+    b.iter(|| rng.random::<u64>());
 }

src/lib.rs

@@ -109,7 +109,7 @@ impl ZipfDistribution {
 
         loop {
             use std::cmp;
-            let u: f64 = hnum + rng.gen::<f64>() * (self.h_integral_x1 - hnum);
+            let u: f64 = hnum + rng.random::<f64>() * (self.h_integral_x1 - hnum);
             // u is uniformly distributed in (h_integral_x1, h_integral_num_elements]
 
             let x: f64 = ZipfDistribution::h_integral_inv(u, self.exponent);
@@ -174,7 +174,7 @@ impl ZipfDistribution {
     }
 }
 
-impl rand::distributions::Distribution<usize> for ZipfDistribution {
+impl rand::distr::Distribution<usize> for ZipfDistribution {
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
         self.next(rng)
     }
@@ -243,7 +243,7 @@ fn helper2(x: f64) -> f64 {
 #[cfg(test)]
 mod test {
     use super::ZipfDistribution;
-    use rand::distributions::Distribution;
+    use rand::distr::Distribution;
 
     #[inline]
     fn test(alpha: f64) {
@@ -252,7 +252,7 @@ mod test {
         // as the alpha increases, we need more samples, since the frequency in the tail grows so low
         let samples = (2.0f64.powf(alpha) * 5000000.0) as usize;
 
-        let mut rng = rand::thread_rng();
+        let mut rng = rand::rng();
         let zipf = ZipfDistribution::new(N, alpha).unwrap();
 
         let harmonic: f64 = (1..=N).map(|n| 1.0 / (n as f64).powf(alpha)).sum();