update rand 0.9 to 0.10

Cargo.toml

@@ -29,14 +29,14 @@ default = ["std", "nalgebra", "rand"]
 std = ["nalgebra?/std", "rand?/std"]
 # at the moment, all nalgebra features needs std
 nalgebra = ["dep:nalgebra", "std"]
-rand = ["dep:rand", "nalgebra?/rand"]
+rand = ["dep:rand", "nalgebra?/rand", "rand?/std_rng"]
 
 [dependencies]
 approx = "0.5.0"
 num-traits = "0.2.14"
 
 [dependencies.rand]
-version = "0.9.0"
+version = "0.10.0"
 optional = true
 default-features = false
 

src/distribution/bernoulli.rs

@@ -88,15 +88,15 @@ impl core::fmt::Display for Bernoulli {
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 impl ::rand::distr::Distribution<bool> for Bernoulli {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> bool {
-        rng.random_bool(self.p())
+        ::rand::RngExt::random_bool(rng, self.p())
     }
 }
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 impl ::rand::distr::Distribution<f64> for Bernoulli {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        rng.sample::<bool, _>(self) as u8 as f64
+        ::rand::RngExt::sample::<bool, _>(rng, self) as u8 as f64
     }
 }
 

src/distribution/binomial.rs

@@ -116,7 +116,7 @@ impl core::fmt::Display for Binomial {
 impl ::rand::distr::Distribution<u64> for Binomial {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> u64 {
         (0..self.n).fold(0, |acc, _| {
-            let n: f64 = rng.random();
+            let n: f64 = ::rand::RngExt::random(rng);
             if n < self.p { acc + 1 } else { acc }
         })
     }
@@ -126,7 +126,7 @@ impl ::rand::distr::Distribution<u64> for Binomial {
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 impl ::rand::distr::Distribution<f64> for Binomial {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        rng.sample::<u64, _>(self) as f64
+        ::rand::RngExt::sample::<u64, _>(rng, self) as f64
     }
 }
 

src/distribution/categorical.rs

@@ -344,7 +344,7 @@ impl Discrete<u64, f64> for Categorical {
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 pub fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, cdf: &[f64]) -> usize {
-    let draw = rng.random::<f64>() * cdf.last().unwrap();
+    let draw = ::rand::RngExt::random::<f64>(rng) * cdf.last().unwrap();
     cdf.iter().position(|val| *val >= draw).unwrap()
 }
 

src/distribution/cauchy.rs

@@ -115,7 +115,8 @@ impl core::fmt::Display for Cauchy {
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 impl ::rand::distr::Distribution<f64> for Cauchy {
     fn sample<R: ::rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
-        self.location + self.scale * (f64::consts::PI * (r.random::<f64>() - 0.5)).tan()
+        let x = ::rand::RngExt::random::<f64>(r);
+        self.location + self.scale * (f64::consts::PI * (x - 0.5)).tan()
     }
 }
 

src/distribution/discrete_uniform.rs

@@ -117,15 +117,15 @@ impl core::fmt::Display for DiscreteUniform {
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 impl ::rand::distr::Distribution<i64> for DiscreteUniform {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> i64 {
-        rng.random_range(self.min..=self.max)
+        ::rand::RngExt::random_range(rng, self.min..=self.max)
     }
 }
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 impl ::rand::distr::Distribution<f64> for DiscreteUniform {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        rng.sample::<i64, _>(self) as f64
+        ::rand::RngExt::sample::<i64, _>(rng, self) as f64
     }
 }
 

src/distribution/gamma.rs

@@ -410,7 +410,7 @@ pub fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, shape: f64, rate:
     let mut afix = 1.0;
     if shape < 1.0 {
         a = shape + 1.0;
-        afix = rng.random::<f64>().powf(1.0 / shape);
+        afix = ::rand::RngExt::random::<f64>(rng).powf(1.0 / shape);
     }
 
     let d = a - 1.0 / 3.0;
@@ -428,7 +428,7 @@ pub fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, shape: f64, rate:
 
         v = v * v * v;
         x = x * x;
-        let u: f64 = rng.random();
+        let u: f64 = ::rand::RngExt::random(rng);
         if u < 1.0 - 0.0331 * x * x || u.ln() < 0.5 * x + d * (1.0 - v + v.ln()) {
             return afix * d * v / rate;
         }

src/distribution/geometric.rs

@@ -3,6 +3,9 @@ use crate::prec;
 use crate::statistics::*;
 use core::f64;
 
+#[cfg(feature = "rand")]
+use rand::RngExt;
+
 /// Implements the
 /// [Geometric](https://en.wikipedia.org/wiki/Geometric_distribution)
 /// distribution

src/distribution/gumbel.rs

@@ -117,7 +117,8 @@ impl core::fmt::Display for Gumbel {
 #[cfg(feature = "rand")]
 impl ::rand::distr::Distribution<f64> for Gumbel {
     fn sample<R: rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
-        self.location - self.scale * ((-(r.random::<f64>())).ln()).ln()
+        let x = ::rand::RngExt::random::<f64>(r);
+        self.location - self.scale * ((-x).ln()).ln()
     }
 }
 

src/distribution/hypergeometric.rs

@@ -168,7 +168,7 @@ impl ::rand::distr::Distribution<u64> for Hypergeometric {
         let mut x = 0;
         loop {
             let p = successes / population;
-            let next: f64 = rng.random();
+            let next: f64 = ::rand::RngExt::random(rng);
             if next < p {
                 x += 1;
                 successes -= 1.0;
@@ -187,7 +187,7 @@ impl ::rand::distr::Distribution<u64> for Hypergeometric {
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 impl ::rand::distr::Distribution<f64> for Hypergeometric {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        rng.sample::<u64, _>(self) as f64
+        ::rand::RngExt::sample::<u64, _>(rng, self) as f64
     }
 }
 

src/distribution/laplace.rs

@@ -115,7 +115,7 @@ impl core::fmt::Display for Laplace {
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 impl ::rand::distr::Distribution<f64> for Laplace {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        let x: f64 = rng.random_range(-0.5..0.5);
+        let x: f64 = ::rand::RngExt::random_range(rng, -0.5..0.5);
         self.location - self.scale * x.signum() * (1. - 2. * x.abs()).ln()
     }
 }
@@ -550,9 +550,12 @@ mod tests {
     #[test]
     fn test_sample() {
         use ::rand::distr::Distribution;
+        use ::rand::rngs::StdRng;
+        use ::rand::SeedableRng;
 
         let l = create_ok(0.1, 0.5);
-        l.sample(&mut ::rand::rng());
+        let mut rng = StdRng::seed_from_u64(42);
+        l.sample(&mut rng);
     }
 
     #[cfg(feature = "rand")]

src/distribution/levy.rs

@@ -114,7 +114,7 @@ impl ::rand::distr::Distribution<f64> for Levy {
         use rand::distr::OpenClosed01;
 
         // Inverse transform sampling
-        let u: f64 = rng.sample(OpenClosed01);
+        let u: f64 = ::rand::RngExt::sample(rng, OpenClosed01);
         self.mu + (0.5 * self.c) / erfc_inv(u).powf(2.0)
     }
 }

src/distribution/multivariate_normal.rs

@@ -414,7 +414,8 @@ where
     /// `μ` is the mean vector
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> OVector<f64, D> {
         let d = crate::distribution::Normal::new(0., 1.).unwrap();
-        let z = OVector::from_distribution_generic(self.mu.shape_generic().0, Const::<1>, &d, rng);
+        let z =
+            OVector::from_fn_generic(self.mu.shape_generic().0, Const::<1>, |_, _| d.sample(rng));
         (&self.cov_chol_decomp * z) + &self.mu
     }
 }

src/distribution/multivariate_students_t.rs

@@ -225,7 +225,7 @@ where
         let s = ChiSquared::new(self.freedom).unwrap();
         let w = (self.freedom / s.sample(rng)).sqrt();
         let (r, c) = self.location.shape_generic();
-        let z = OVector::<f64, D>::from_distribution_generic(r, c, &d, rng);
+        let z = OVector::<f64, D>::from_fn_generic(r, c, |_, _| d.sample(rng));
         (w * &self.scale_chol_decomp * z) + &self.location
     }
 }

src/distribution/negative_binomial.rs

@@ -493,7 +493,7 @@ mod tests {
     #[cfg(all(feature = "rand", feature = "std"))]
     fn test_sample() {
         use crate::prec;
-        use rand::{distr::Distribution, SeedableRng, rngs::StdRng};
+        use rand::{distr::Distribution, rngs::StdRng, SeedableRng};
 
         let dist = NegativeBinomial::new(4.0, 0.5).unwrap();
         let mut rng = StdRng::seed_from_u64(1600);

src/distribution/pareto.rs

@@ -120,7 +120,7 @@ impl ::rand::distr::Distribution<f64> for Pareto {
         use rand::distr::OpenClosed01;
 
         // Inverse transform sampling
-        let u: f64 = rng.sample(OpenClosed01);
+        let u: f64 = ::rand::RngExt::sample(rng, OpenClosed01);
         self.scale * u.powf(-1.0 / self.shape)
     }
 }

src/distribution/poisson.rs

@@ -302,6 +302,8 @@ impl Discrete<u64, f64> for Poisson {
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 pub fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, lambda: f64) -> f64 {
+    use rand::RngExt;
+
     if lambda < 30.0 {
         let limit = (-lambda).exp();
         let mut count = 0.0;

src/distribution/triangular.rs

@@ -425,7 +425,7 @@ impl Continuous<f64, f64> for Triangular {
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, min: f64, max: f64, mode: f64) -> f64 {
-    let f: f64 = rng.random();
+    let f: f64 = ::rand::RngExt::random(rng);
     if f < (mode - min) / (max - min) {
         min + (f * (max - min) * (mode - min)).sqrt()
     } else {

src/distribution/uniform.rs

@@ -154,7 +154,7 @@ impl core::fmt::Display for Uniform {
 impl ::rand::distr::Distribution<f64> for Uniform {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         let d = rand::distr::Uniform::new_inclusive(self.min, self.max).unwrap();
-        rng.sample(d)
+        ::rand::RngExt::sample(rng, d)
     }
 }
 

src/distribution/weibull.rs

@@ -126,7 +126,7 @@ impl core::fmt::Display for Weibull {
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 impl ::rand::distr::Distribution<f64> for Weibull {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        let x: f64 = rng.random();
+        let x: f64 = ::rand::RngExt::random(rng);
         self.scale * (-x.ln()).powf(1.0 / self.shape)
     }
 }

src/distribution/ziggurat.rs

@@ -1,6 +1,6 @@
 use super::ziggurat_tables;
-use rand::Rng;
 use rand::distr::Open01;
+use rand::{Rng, RngExt};
 
 pub fn sample_std_normal<R: Rng + ?Sized>(rng: &mut R) -> f64 {
     #[inline]