migrate to num-traits, raise MSRV to 1.36

.travis.yml

@@ -6,8 +6,8 @@ sudo: false
 
 matrix:
   include:
-    - rust: 1.32.0
-      name: "Linux, 1.32.0"
+    - rust: 1.36.0
+      name: "Linux, 1.36.0"
       env: ALLOC=0
       os: linux
 

rand_distr/Cargo.toml

@@ -20,9 +20,17 @@ travis-ci = { repository = "rust-random/rand" }
 appveyor = { repository = "rust-random/rand" }
 
 [dependencies]
-rand = { path = "..", version = "0.7" }
+rand = { path = "..", version = "0.7", default-features = false }
+num-traits = { version = "0.2", default-features = false, features = ["libm"] }
+
+[features]
+default = ["std"]
+std = ["rand/std", "num-traits/std", "alloc"]
+alloc = ["rand/alloc"]
 
 [dev-dependencies]
 rand_pcg = { version = "0.2", path = "../rand_pcg" }
+# For inline examples
+rand = { path = "..", version = "0.7", default-features = false, features = ["std_rng", "std"] }
 # Histogram implementation for testing uniformity
 average = "0.10.3"

rand_distr/src/binomial.rs

@@ -11,7 +11,7 @@
 
 use crate::{Distribution, Uniform};
 use rand::Rng;
-use std::{error, fmt};
+use core::fmt;
 
 /// The binomial distribution `Binomial(n, p)`.
 ///
@@ -53,7 +53,8 @@ impl fmt::Display for Error {
     }
 }
 
-impl error::Error for Error {}
+#[cfg(feature = "std")]
+impl std::error::Error for Error {}
 
 impl Binomial {
     /// Construct a new `Binomial` with the given shape parameters `n` (number
@@ -72,7 +73,7 @@ impl Binomial {
 /// Convert a `f64` to an `i64`, panicing on overflow.
 // In the future (Rust 1.34), this might be replaced with `TryFrom`.
 fn f64_to_i64(x: f64) -> i64 {
-    assert!(x < (::std::i64::MAX as f64));
+    assert!(x < (core::i64::MAX as f64));
     x as i64
 }
 
@@ -106,7 +107,7 @@ impl Distribution<u64> for Binomial {
         // Ranlib uses 30, and GSL uses 14.
         const BINV_THRESHOLD: f64 = 10.;
 
-        if (self.n as f64) * p < BINV_THRESHOLD && self.n <= (::std::i32::MAX as u64) {
+        if (self.n as f64) * p < BINV_THRESHOLD && self.n <= (core::i32::MAX as u64) {
             // Use the BINV algorithm.
             let s = p / q;
             let a = ((self.n + 1) as f64) * s;

rand_distr/src/cauchy.rs

@@ -9,10 +9,10 @@
 
 //! The Cauchy distribution.
 
-use crate::utils::Float;
+use num_traits::{Float, FloatConst};
 use crate::{Distribution, Standard};
 use rand::Rng;
-use std::{error, fmt};
+use core::fmt;
 
 /// The Cauchy distribution `Cauchy(median, scale)`.
 ///
@@ -52,30 +52,31 @@ impl fmt::Display for Error {
     }
 }
 
-impl error::Error for Error {}
+#[cfg(feature = "std")]
+impl std::error::Error for Error {}
 
-impl<N: Float> Cauchy<N>
+impl<N: Float + FloatConst> Cauchy<N>
 where Standard: Distribution<N>
 {
     /// Construct a new `Cauchy` with the given shape parameters
     /// `median` the peak location and `scale` the scale factor.
     pub fn new(median: N, scale: N) -> Result<Cauchy<N>, Error> {
-        if !(scale > N::from(0.0)) {
+        if !(scale > N::zero()) {
             return Err(Error::ScaleTooSmall);
         }
         Ok(Cauchy { median, scale })
     }
 }
 
-impl<N: Float> Distribution<N> for Cauchy<N>
+impl<N: Float + FloatConst> Distribution<N> for Cauchy<N>
 where Standard: Distribution<N>
 {
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
         // sample from [0, 1)
         let x = Standard.sample(rng);
         // get standard cauchy random number
         // note that π/2 is not exactly representable, even if x=0.5 the result is finite
-        let comp_dev = (N::pi() * x).tan();
+        let comp_dev = (N::PI() * x).tan();
         // shift and scale according to parameters
         self.median + self.scale * comp_dev
     }
@@ -108,10 +109,12 @@ mod test {
             sum += numbers[i];
         }
         let median = median(&mut numbers);
-        println!("Cauchy median: {}", median);
+        #[cfg(feature = "std")]
+        std::println!("Cauchy median: {}", median);
         assert!((median - 10.0).abs() < 0.4); // not 100% certain, but probable enough
         let mean = sum / 1000.0;
-        println!("Cauchy mean: {}", mean);
+        #[cfg(feature = "std")]
+        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
     }
@@ -130,7 +133,7 @@ mod test {
 
     #[test]
     fn value_stability() {
-        fn gen_samples<N: Float + core::fmt::Debug>(m: N, s: N, buf: &mut [N])
+        fn gen_samples<N: Float + FloatConst + core::fmt::Debug>(m: N, s: N, buf: &mut [N])
         where Standard: Distribution<N> {
             let distr = Cauchy::new(m, s).unwrap();
             let mut rng = crate::test::rng(353);

rand_distr/src/dirichlet.rs

@@ -8,11 +8,12 @@
 // except according to those terms.
 
 //! The dirichlet distribution.
-
-use crate::utils::Float;
+#![cfg(feature = "alloc")]
+use num_traits::Float;
 use crate::{Distribution, Exp1, Gamma, Open01, StandardNormal};
 use rand::Rng;
-use std::{error, fmt};
+use core::fmt;
+use alloc::{vec, vec::Vec};
 
 /// The Dirichlet distribution `Dirichlet(alpha)`.
 ///
@@ -58,7 +59,8 @@ impl fmt::Display for Error {
     }
 }
 
-impl error::Error for Error {}
+#[cfg(feature = "std")]
+impl std::error::Error for Error {}
 
 impl<N: Float> Dirichlet<N>
 where
@@ -76,7 +78,7 @@ where
             return Err(Error::AlphaTooShort);
         }
         for &ai in &a {
-            if !(ai > N::from(0.0)) {
+            if !(ai > N::zero()) {
                 return Err(Error::AlphaTooSmall);
             }
         }
@@ -89,7 +91,7 @@ where
     /// Requires `size >= 2`.
     #[inline]
     pub fn new_with_size(alpha: N, size: usize) -> Result<Dirichlet<N>, Error> {
-        if !(alpha > N::from(0.0)) {
+        if !(alpha > N::zero()) {
             return Err(Error::AlphaTooSmall);
         }
         if size < 2 {
@@ -109,17 +111,17 @@ where
 {
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<N> {
         let n = self.alpha.len();
-        let mut samples = vec![N::from(0.0); n];
-        let mut sum = N::from(0.0);
+        let mut samples = vec![N::zero(); n];
+        let mut sum = N::zero();
 
         for (s, &a) in samples.iter_mut().zip(self.alpha.iter()) {
-            let g = Gamma::new(a, N::from(1.0)).unwrap();
+            let g = Gamma::new(a, N::one()).unwrap();
             *s = g.sample(rng);
-            sum += *s;
+            sum =  sum + (*s);
         }
-        let invacc = N::from(1.0) / sum;
+        let invacc = N::one() / sum;
         for s in samples.iter_mut() {
-            *s *= invacc;
+            *s = (*s)*invacc;
         }
         samples
     }

rand_distr/src/exponential.rs

@@ -9,10 +9,11 @@
 
 //! The exponential distribution.
 
-use crate::utils::{ziggurat, Float};
+use crate::utils::ziggurat;
+use num_traits::Float;
 use crate::{ziggurat_tables, Distribution};
 use rand::Rng;
-use std::{error, fmt};
+use core::fmt;
 
 /// Samples floating-point numbers according to the exponential distribution,
 /// with rate parameter `λ = 1`. This is equivalent to `Exp::new(1.0)` or
@@ -110,7 +111,8 @@ impl fmt::Display for Error {
     }
 }
 
-impl error::Error for Error {}
+#[cfg(feature = "std")]
+impl std::error::Error for Error {}
 
 impl<N: Float> Exp<N>
 where Exp1: Distribution<N>
@@ -119,11 +121,11 @@ where Exp1: Distribution<N>
     /// `lambda`.
     #[inline]
     pub fn new(lambda: N) -> Result<Exp<N>, Error> {
-        if !(lambda > N::from(0.0)) {
+        if !(lambda > N::zero()) {
             return Err(Error::LambdaTooSmall);
         }
         Ok(Exp {
-            lambda_inverse: N::from(1.0) / lambda,
+            lambda_inverse: N::one() / lambda,
         })
     }
 }