---

yaml
---
r: 56629
b: refs/heads/auto
c: 0c34cab
h: refs/heads/master
i:
  56627: 1511797
v: v3

Author: sanxiyn

[refs]

@@ -14,6 +14,6 @@ refs/heads/try3: 9387340aab40a73e8424c48fd42f0c521a4875c0
 refs/tags/release-0.3.1: 495bae036dfe5ec6ceafd3312b4dca48741e845b
 refs/tags/release-0.4: e828ea2080499553b97dfe33b3f4d472b4562ad7
 refs/tags/release-0.5: 7e3bcfbf21278251ee936ad53e92e9b719702d73
-refs/heads/auto: d3e7c746dddf24935bd55330d20b692fb0e691a6
+refs/heads/auto: 0c34cab3db76d100ea7bbe4cd776f27b5f971d2c
 refs/heads/servo: af82457af293e2a842ba6b7759b70288da276167
 refs/tags/release-0.6: b4ebcfa1812664df5e142f0134a5faea3918544c

branches/auto/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;