Resolve Clippy lints, replace rand::rngs::mock::StepRng

distr_test/tests/cdf.rs

@@ -395,7 +395,7 @@ fn geometric() {
 #[test]
 fn hypergeometric() {
     fn cdf(x: i64, n: u64, k: u64, n_: u64) -> f64 {
-        let min = if n_ + k > n { n_ + k - n } else { 0 };
+        let min = (n_ + k).saturating_sub(n);
         let max = k.min(n_);
         if x < min as i64 {
             return 0.0;

distr_test/tests/ks/mod.rs

@@ -112,8 +112,8 @@ pub fn test_continuous(seed: u64, dist: impl Distribution<f64>, cdf: impl Fn(f64
 
     let critical_value = critical_value();
 
-    println!("KS statistic: {}", ks_statistic);
-    println!("Critical value: {}", critical_value);
+    println!("KS statistic: {ks_statistic}");
+    println!("Critical value: {critical_value}");
     assert!(ks_statistic < critical_value);
 }
 
@@ -131,7 +131,7 @@ where
     // This critical value is bigger than it could be for discrete distributions, but because of large sample sizes this should not matter too much
     let critical_value = critical_value();
 
-    println!("KS statistic: {}", ks_statistic);
-    println!("Critical value: {}", critical_value);
+    println!("KS statistic: {ks_statistic}");
+    println!("Critical value: {critical_value}");
     assert!(ks_statistic < critical_value);
 }

distr_test/tests/skew_normal.rs

@@ -257,6 +257,9 @@ fn owen_t(h: f64, a: f64) -> f64 {
 }
 
 fn normal_cdf(x: f64, mean: f64, std_dev: f64) -> f64 {
+    // f64::erfc from feature `float_erf` may be used over special::Primitive::erfc
+    #![allow(unstable_name_collisions)]
+
     0.5 * ((mean - x) / (std_dev * core::f64::consts::SQRT_2)).erfc()
 }
 

src/beta.rs

@@ -287,7 +287,7 @@ mod test {
         let beta = Beta::<f64>::new(1e-3, 1e-3).unwrap();
         let mut rng = crate::test::rng(206);
         for i in 0..1000 {
-            assert!(!beta.sample(&mut rng).is_nan(), "failed at i={}", i);
+            assert!(!beta.sample(&mut rng).is_nan(), "failed at i={i}");
         }
     }
 

src/cauchy.rs

@@ -141,11 +141,11 @@ mod test {
         }
         let median = median(&mut numbers);
         #[cfg(feature = "std")]
-        std::println!("Cauchy median: {}", median);
+        std::println!("Cauchy median: {median}");
         assert!((median - 10.0).abs() < 0.4); // not 100% certain, but probable enough
         let mean = sum / 1000.0;
         #[cfg(feature = "std")]
-        std::println!("Cauchy mean: {}", mean);
+        std::println!("Cauchy mean: {mean}");
         // for a Cauchy distribution the mean should not converge
         assert!((mean - 10.0).abs() > 0.4); // not 100% certain, but probable enough
     }

src/triangular.rs

@@ -113,11 +113,12 @@ where
 #[cfg(test)]
 mod test {
     use super::*;
-    use rand::{rngs::mock, Rng};
+    use crate::utils::ConstRng;
+    use rand::Rng;
 
     #[test]
     fn test_triangular() {
-        let mut half_rng = mock::StepRng::new(0x8000_0000_0000_0000, 0);
+        let mut half_rng = ConstRng(0x8000_0000_0000_0000);
         assert_eq!(half_rng.random::<f64>(), 0.5);
         for &(min, max, mode, median) in &[
             (-1., 1., 0., 0.),
@@ -128,7 +129,7 @@ mod test {
             (-4., -0.5, -2., -4.0 + 3.5f64.sqrt()),
         ] {
             #[cfg(feature = "std")]
-            std::println!("{} {} {} {}", min, max, mode, median);
+            std::println!("{min} {max} {mode} {median}");
             let distr = Triangular::new(min, max, mode).unwrap();
             // Test correct value at median:
             assert_eq!(distr.sample(&mut half_rng), median);

src/utils.rs

@@ -14,6 +14,24 @@ use num_traits::Float; // Used for `no_std` to get `f64::abs()` working before `
 use rand::distr::hidden_export::IntoFloat;
 use rand::Rng;
 
+/// An RNG yielding a constant value
+#[cfg(test)]
+pub(crate) struct ConstRng(pub(crate) u64);
+#[cfg(test)]
+impl rand::RngCore for ConstRng {
+    fn next_u32(&mut self) -> u32 {
+        self.next_u64() as u32
+    }
+
+    fn next_u64(&mut self) -> u64 {
+        self.0
+    }
+
+    fn fill_bytes(&mut self, _: &mut [u8]) {
+        unimplemented!()
+    }
+}
+
 /// Sample a random number using the Ziggurat method (specifically the
 /// ZIGNOR variant from Doornik 2005). Most of the arguments are
 /// directly from the paper:
@@ -25,7 +43,7 @@ use rand::Rng;
 /// * `F_DIFF`: precomputed values of $f(x_i) - f(x_{i+1})$
 /// * `pdf`: the probability density function
 /// * `zero_case`: manual sampling from the tail when we chose the
-///    bottom box (i.e. i == 0)
+///   bottom box (i.e. i == 0)
 #[inline(always)] // Forced inlining improves the perf by 25-50%
 pub(crate) fn ziggurat<R: Rng + ?Sized, P, Z>(
     rng: &mut R,