std::rand: remove the fn main() from the examples.

Author: huonw

src/libstd/rand/distributions/exponential.rs

@@ -60,11 +60,9 @@ impl Rand for Exp1 {
 /// use std::rand;
 /// use std::rand::distributions::{Exp, IndependentSample};
 ///
-/// fn main() {
-///     let exp = Exp::new(2.0);
-///     let v = exp.ind_sample(&mut rand::task_rng());
-///     println!("{} is from a Exp(2) distribution", v);
-/// }
+/// let exp = Exp::new(2.0);
+/// let v = exp.ind_sample(&mut rand::task_rng());
+/// println!("{} is from a Exp(2) distribution", v);
 /// ```
 pub struct Exp {
     /// `lambda` stored as `1/lambda`, since this is what we scale by.

src/libstd/rand/distributions/gamma.rs

@@ -37,11 +37,9 @@ use num;
 /// use std::rand;
 /// use std::rand::distributions::{IndependentSample, Gamma};
 ///
-/// fn main() {
-///     let gamma = Gamma::new(2.0, 5.0);
-///     let v = gamma.ind_sample(&mut rand::task_rng());
-///     println!("{} is from a Gamma(2, 5) distribution", v);
-/// }
+/// let gamma = Gamma::new(2.0, 5.0);
+/// let v = gamma.ind_sample(&mut rand::task_rng());
+/// println!("{} is from a Gamma(2, 5) distribution", v);
 /// ```
 ///
 /// [1]: George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method
@@ -183,11 +181,9 @@ impl IndependentSample<f64> for GammaLargeShape {
 /// use std::rand;
 /// use std::rand::distributions::{ChiSquared, IndependentSample};
 ///
-/// fn main() {
-///     let chi = ChiSquared::new(11.0);
-///     let v = chi.ind_sample(&mut rand::task_rng());
-///     println!("{} is from a χ²(11) distribution", v)
-/// }
+/// let chi = ChiSquared::new(11.0);
+/// let v = chi.ind_sample(&mut rand::task_rng());
+/// println!("{} is from a χ²(11) distribution", v)
 /// ```
 pub enum ChiSquared {
     // k == 1, Gamma(alpha, ..) is particularly slow for alpha < 1,
@@ -237,11 +233,9 @@ impl IndependentSample<f64> for ChiSquared {
 /// use std::rand;
 /// use std::rand::distributions::{FisherF, IndependentSample};
 ///
-/// fn main() {
-///     let f = FisherF::new(2.0, 32.0);
-///     let v = f.ind_sample(&mut rand::task_rng());
-///     println!("{} is from an F(2, 32) distribution", v)
-/// }
+/// let f = FisherF::new(2.0, 32.0);
+/// let v = f.ind_sample(&mut rand::task_rng());
+/// println!("{} is from an F(2, 32) distribution", v)
 /// ```
 pub struct FisherF {
     priv numer: ChiSquared,
@@ -283,11 +277,9 @@ impl IndependentSample<f64> for FisherF {
 /// use std::rand;
 /// use std::rand::distributions::{StudentT, IndependentSample};
 ///
-/// fn main() {
-///     let t = StudentT::new(11.0);
-///     let v = t.ind_sample(&mut rand::task_rng());
-///     println!("{} is from a t(11) distribution", v)
-/// }
+/// let t = StudentT::new(11.0);
+/// let v = t.ind_sample(&mut rand::task_rng());
+/// println!("{} is from a t(11) distribution", v)
 /// ```
 pub struct StudentT {
     priv chi: ChiSquared,

src/libstd/rand/distributions/mod.rs

@@ -94,15 +94,13 @@ pub struct Weighted<T> {
 /// use std::rand;
 /// use std::rand::distributions::{Weighted, WeightedChoice, IndependentSample};
 ///
-/// fn main() {
-///     let wc = WeightedChoice::new(~[Weighted { weight: 2, item: 'a' },
-///                                    Weighted { weight: 4, item: 'b' },
-///                                    Weighted { weight: 1, item: 'c' }]);
-///     let mut rng = rand::task_rng();
-///     for _ in range(0, 16) {
-///          // on average prints 'a' 4 times, 'b' 8 and 'c' twice.
-///          println!("{}", wc.ind_sample(&mut rng));
-///     }
+/// let wc = WeightedChoice::new(~[Weighted { weight: 2, item: 'a' },
+///                                Weighted { weight: 4, item: 'b' },
+///                                Weighted { weight: 1, item: 'c' }]);
+/// let mut rng = rand::task_rng();
+/// for _ in range(0, 16) {
+///      // on average prints 'a' 4 times, 'b' 8 and 'c' twice.
+///      println!("{}", wc.ind_sample(&mut rng));
 /// }
 /// ```
 pub struct WeightedChoice<T> {

src/libstd/rand/distributions/normal.rs

@@ -76,12 +76,10 @@ impl Rand for StandardNormal {
 /// use std::rand;
 /// use std::rand::distributions::{Normal, IndependentSample};
 ///
-/// fn main() {
-///     // mean 2, standard deviation 3
-///     let normal = Normal::new(2.0, 3.0);
-///     let v = normal.ind_sample(&mut rand::task_rng());
-///     println!("{} is from a N(2, 9) distribution", v)
-/// }
+/// // mean 2, standard deviation 3
+/// let normal = Normal::new(2.0, 3.0);
+/// let v = normal.ind_sample(&mut rand::task_rng());
+/// println!("{} is from a N(2, 9) distribution", v)
 /// ```
 pub struct Normal {
     priv mean: f64,
@@ -120,12 +118,10 @@ impl IndependentSample<f64> for Normal {
 /// use std::rand;
 /// use std::rand::distributions::{LogNormal, IndependentSample};
 ///
-/// fn main() {
-///     // mean 2, standard deviation 3
-///     let log_normal = LogNormal::new(2.0, 3.0);
-///     let v = log_normal.ind_sample(&mut rand::task_rng());
-///     println!("{} is from an ln N(2, 9) distribution", v)
-/// }
+/// // mean 2, standard deviation 3
+/// let log_normal = LogNormal::new(2.0, 3.0);
+/// let v = log_normal.ind_sample(&mut rand::task_rng());
+/// println!("{} is from an ln N(2, 9) distribution", v)
 /// ```
 pub struct LogNormal {
     priv norm: Normal