Remove random()

See #293.

src/lib.rs

@@ -10,12 +10,11 @@
 
 //! Utilities for random number generation
 //!
-//! The key functions are `random()` and `Rng::gen()`. These are polymorphic and
-//! so can be used to generate any type supporting the [`Uniform`] distribution
-//! (i.e. `T` where `Uniform`: `Distribution<T>`). Type inference
-//! means that often a simple call to `rand::random()` or `rng.gen()` will
-//! suffice, but sometimes an annotation is required, e.g.
-//! `rand::random::<f64>()`.
+//! The key function is `Rng::gen()`. It is polymorphic and so can be used to
+//! generate any type supporting the [`Uniform`] distribution (i.e. `T` where
+//! `Uniform`: `Distribution<T>`). Type inference means that often a simple call
+//! to `rng.gen()` will suffice, but sometimes an annotation is required, e.g.
+//! `rng.gen::<f64>()`.
 //!
 //! See the `distributions` submodule for sampling random numbers from
 //! distributions like normal and exponential.
@@ -88,11 +87,6 @@
 //! }
 //! ```
 //!
-//! ```rust
-//! let tuple = rand::random::<(f64, char)>();
-//! println!("{:?}", tuple)
-//! ```
-//!
 //! ## Monte Carlo estimation of π
 //!
 //! For this example, imagine we have a square with sides of length 2 and a unit
@@ -289,7 +283,7 @@ pub use error::{ErrorKind, Error};
 
 // convenience and derived rngs
 #[cfg(feature="std")] pub use entropy_rng::EntropyRng;
-#[cfg(feature="std")] pub use thread_rng::{ThreadRng, thread_rng, random};
+#[cfg(feature="std")] pub use thread_rng::{ThreadRng, thread_rng};
 
 use distributions::{Distribution, Uniform, Range};
 use distributions::range::SampleRange;

src/thread_rng.rs

@@ -13,8 +13,7 @@
 use std::cell::RefCell;
 use std::rc::Rc;
 
-use {RngCore, CryptoRng, StdRng, SeedableRng, EntropyRng};
-use {Distribution, Uniform, Rng, Error};
+use {RngCore, CryptoRng, StdRng, SeedableRng, EntropyRng, Error};
 use reseeding::ReseedingRng;
 
 // Number of generated bytes after which to reseed `TreadRng`.
@@ -99,57 +98,9 @@ impl RngCore for ThreadRng {
 
 impl CryptoRng for ThreadRng {}
 
-/// Generates a random value using the thread-local random number generator.
-/// 
-/// This is simply a shortcut for `thread_rng().gen()`. See [`thread_rng`] for
-/// documentation of the entropy source and [`Rand`] for documentation of
-/// distributions and type-specific generation.
-///
-/// # Examples
-///
-/// ```
-/// let x = rand::random::<u8>();
-/// println!("{}", x);
-///
-/// let y = rand::random::<f64>();
-/// println!("{}", y);
-///
-/// if rand::random() { // generates a boolean
-///     println!("Better lucky than good!");
-/// }
-/// ```
-///
-/// If you're calling `random()` in a loop, caching the generator as in the
-/// following example can increase performance.
-///
-/// ```
-/// use rand::Rng;
-///
-/// let mut v = vec![1, 2, 3];
-///
-/// for x in v.iter_mut() {
-///     *x = rand::random()
-/// }
-///
-/// // can be made faster by caching thread_rng
-///
-/// let mut rng = rand::thread_rng();
-///
-/// for x in v.iter_mut() {
-///     *x = rng.gen();
-/// }
-/// ```
-/// 
-/// [`thread_rng`]: fn.thread_rng.html
-/// [`Rand`]: trait.Rand.html
-#[inline]
-pub fn random<T>() -> T where Uniform: Distribution<T> {
-    thread_rng().gen()
-}
-
 #[cfg(test)]
 mod test {
-    use super::*;
+    use Rng;
 
     #[test]
     #[cfg(feature="std")]
@@ -163,18 +114,4 @@ mod test {
         assert_eq!(v, b);
         assert_eq!(r.gen_range(0, 1), 0);
     }
-
-    #[test]
-    fn test_random() {
-        // not sure how to test this aside from just getting some values
-        let _n : usize = random();
-        let _f : f32 = random();
-        let _o : Option<Option<i8>> = random();
-        let _many : ((),
-                     (usize,
-                      isize,
-                      Option<(u32, (bool,))>),
-                     (u8, i8, u16, i16, u32, i32, u64, i64),
-                     (f32, (f64, (f64,)))) = random();
-    }
 }