---

yaml
---
r: 59863
b: refs/heads/master
c: 0c34cab
h: refs/heads/master
i:
  59861: 22b4437
  59859: 8d96a94
  59855: e7fee35
v: v3

Author: sanxiyn

[refs]

@@ -1,5 +1,5 @@
 ---
-refs/heads/master: d3e7c746dddf24935bd55330d20b692fb0e691a6
+refs/heads/master: 0c34cab3db76d100ea7bbe4cd776f27b5f971d2c
 refs/heads/snap-stage1: e33de59e47c5076a89eadeb38f4934f58a3618a6
 refs/heads/snap-stage3: 2d28d645422c1617be58c8ca7ad9a457264ca850
 refs/heads/try: c50a9d5b664478e533ba1d1d353213d70c8ad589

trunk/src/etc/ziggurat_tables.py

@@ -16,9 +16,6 @@ and so can be used to generate any type that implements `Rand`. 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::<float>()`.
 
-See the `distributions` submodule for sampling random numbers from
-distributions like normal and exponential.
-
 # Examples
 ~~~
 use core::rand::RngUtil;
@@ -50,9 +47,6 @@ use util;
 use vec;
 use libc::size_t;
 
-#[path="rand/distributions.rs"]
-pub mod distributions;
-
 /// A type that can be randomly generated using an Rng
 pub trait Rand {
     fn rand<R: Rng>(rng: &R) -> Self;