src/librand/lib.rs - third_party/rust - Git at Google

 // Copyright 2013-2014 The Rust Project Developers. See the COPYRIGHT
 // file at the top-level directory of this distribution and at
 // http://rust-lang.org/COPYRIGHT.
 //
 // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
 // http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
 // <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
 // option. This file may not be copied, modified, or distributed
 // except according to those terms.

 //! Interface to random number generators in Rust.
 //!
 //! This is an experimental library which lives underneath the standard library
 //! in its dependency chain. This library is intended to define the interface
 //! for random number generation and also provide utilities around doing so. It
 //! is not recommended to use this library directly, but rather the official
 //! interface through `std::rand`.

 #![crate_name = "rand"]
 #![crate_type = "rlib"]
 #![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
        html_favicon_url = "https://doc.rust-lang.org/favicon.ico",
        html_root_url = "https://doc.rust-lang.org/nightly/",
        html_playground_url = "https://play.rust-lang.org/",
        test(attr(deny(warnings))))]
 #![cfg_attr(not(stage0), deny(warnings))]
 #![no_std]
 #![unstable(feature = "rand",
             reason = "use `rand` from crates.io",
             issue = "27703")]
 #![feature(core_intrinsics)]
 #![feature(staged_api)]
 #![feature(step_by)]
 #![feature(custom_attribute)]
 #![allow(unused_attributes)]

 #![cfg_attr(not(test), feature(core_float))] // only necessary for no_std
 #![cfg_attr(test, feature(test, rand))]

 #![allow(deprecated)]

 #[cfg(test)]
 #[macro_use]
 extern crate std;

 use core::f64;
 use core::intrinsics;
 use core::marker::PhantomData;

 pub use isaac::{Isaac64Rng, IsaacRng};
 pub use chacha::ChaChaRng;

 use distributions::{IndependentSample, Range};
 use distributions::range::SampleRange;

 #[cfg(test)]
 const RAND_BENCH_N: u64 = 100;

 pub mod distributions;
 pub mod isaac;
 pub mod chacha;
 pub mod reseeding;
 mod rand_impls;

 // Temporary trait to implement a few floating-point routines
 // needed by librand; this is necessary because librand doesn't
 // depend on libstd.  This will go away when librand is integrated
 // into libstd.
 #[doc(hidden)]
 trait FloatMath: Sized {
     fn exp(self) -> Self;
     fn ln(self) -> Self;
     fn sqrt(self) -> Self;
     fn powf(self, n: Self) -> Self;
 }

 impl FloatMath for f64 {
     #[inline]
     fn exp(self) -> f64 {
         unsafe { intrinsics::expf64(self) }
     }

     #[inline]
     fn ln(self) -> f64 {
         unsafe { intrinsics::logf64(self) }
     }

     #[inline]
     fn powf(self, n: f64) -> f64 {
         unsafe { intrinsics::powf64(self, n) }
     }

     #[inline]
     fn sqrt(self) -> f64 {
         if self < 0.0 {
             f64::NAN
         } else {
             unsafe { intrinsics::sqrtf64(self) }
         }
     }
 }

 /// A type that can be randomly generated using an `Rng`.
 #[doc(hidden)]
 pub trait Rand: Sized {
     /// Generates a random instance of this type using the specified source of
     /// randomness.
     fn rand<R: Rng>(rng: &mut R) -> Self;
 }

 /// A random number generator.
 pub trait Rng: Sized {
     /// Return the next random u32.
     ///
     /// This rarely needs to be called directly, prefer `r.gen()` to
     /// `r.next_u32()`.
     // FIXME #7771: Should be implemented in terms of next_u64
     fn next_u32(&mut self) -> u32;

     /// Return the next random u64.
     ///
     /// By default this is implemented in terms of `next_u32`. An
     /// implementation of this trait must provide at least one of
     /// these two methods. Similarly to `next_u32`, this rarely needs
     /// to be called directly, prefer `r.gen()` to `r.next_u64()`.
     fn next_u64(&mut self) -> u64 {
         ((self.next_u32() as u64) << 32) | (self.next_u32() as u64)
     }

     /// Return the next random f32 selected from the half-open
     /// interval `[0, 1)`.
     ///
     /// By default this is implemented in terms of `next_u32`, but a
     /// random number generator which can generate numbers satisfying
     /// the requirements directly can overload this for performance.
     /// It is required that the return value lies in `[0, 1)`.
     ///
     /// See `Closed01` for the closed interval `[0,1]`, and
     /// `Open01` for the open interval `(0,1)`.
     fn next_f32(&mut self) -> f32 {
         const MANTISSA_BITS: usize = 24;
         const IGNORED_BITS: usize = 8;
         const SCALE: f32 = (1u64 << MANTISSA_BITS) as f32;

         // using any more than `MANTISSA_BITS` bits will
         // cause (e.g.) 0xffff_ffff to correspond to 1
         // exactly, so we need to drop some (8 for f32, 11
         // for f64) to guarantee the open end.
         (self.next_u32() >> IGNORED_BITS) as f32 / SCALE
     }

     /// Return the next random f64 selected from the half-open
     /// interval `[0, 1)`.
     ///
     /// By default this is implemented in terms of `next_u64`, but a
     /// random number generator which can generate numbers satisfying
     /// the requirements directly can overload this for performance.
     /// It is required that the return value lies in `[0, 1)`.
     ///
     /// See `Closed01` for the closed interval `[0,1]`, and
     /// `Open01` for the open interval `(0,1)`.
     fn next_f64(&mut self) -> f64 {
         const MANTISSA_BITS: usize = 53;
         const IGNORED_BITS: usize = 11;
         const SCALE: f64 = (1u64 << MANTISSA_BITS) as f64;

         (self.next_u64() >> IGNORED_BITS) as f64 / SCALE
     }

     /// Fill `dest` with random data.
     ///
     /// This has a default implementation in terms of `next_u64` and
     /// `next_u32`, but should be overridden by implementations that
     /// offer a more efficient solution than just calling those
     /// methods repeatedly.
     ///
     /// This method does *not* have a requirement to bear any fixed
     /// relationship to the other methods, for example, it does *not*
     /// have to result in the same output as progressively filling
     /// `dest` with `self.gen::<u8>()`, and any such behaviour should
     /// not be relied upon.
     ///
     /// This method should guarantee that `dest` is entirely filled
     /// with new data, and may panic if this is impossible
     /// (e.g. reading past the end of a file that is being used as the
     /// source of randomness).
     fn fill_bytes(&mut self, dest: &mut [u8]) {
         // this could, in theory, be done by transmuting dest to a
         // [u64], but this is (1) likely to be undefined behaviour for
         // LLVM, (2) has to be very careful about alignment concerns,
         // (3) adds more `unsafe` that needs to be checked, (4)
         // probably doesn't give much performance gain if
         // optimisations are on.
         let mut count = 0;
         let mut num = 0;
         for byte in dest {
             if count == 0 {
                 // we could micro-optimise here by generating a u32 if
                 // we only need a few more bytes to fill the vector
                 // (i.e. at most 4).
                 num = self.next_u64();
                 count = 8;
             }

             *byte = (num & 0xff) as u8;
             num >>= 8;
             count -= 1;
         }
     }

     /// Return a random value of a `Rand` type.
     #[inline(always)]
     fn gen<T: Rand>(&mut self) -> T {
         Rand::rand(self)
     }

     /// Return an iterator that will yield an infinite number of randomly
     /// generated items.
     fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> {
         Generator {
             rng: self,
             _marker: PhantomData,
         }
     }

     /// Generate a random value in the range [`low`, `high`).
     ///
     /// This is a convenience wrapper around
     /// `distributions::Range`. If this function will be called
     /// repeatedly with the same arguments, one should use `Range`, as
     /// that will amortize the computations that allow for perfect
     /// uniformity, as they only happen on initialization.
     ///
     /// # Panics
     ///
     /// Panics if `low >= high`.
     fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T {
         assert!(low < high, "Rng.gen_range called with low >= high");
         Range::new(low, high).ind_sample(self)
     }

     /// Return a bool with a 1 in n chance of true
     fn gen_weighted_bool(&mut self, n: usize) -> bool {
         n <= 1 || self.gen_range(0, n) == 0
     }

     /// Return an iterator of random characters from the set A-Z,a-z,0-9.
     fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> {
         AsciiGenerator { rng: self }
     }

     /// Return a random element from `values`.
     ///
     /// Return `None` if `values` is empty.
     fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> {
         if values.is_empty() {
             None
         } else {
             Some(&values[self.gen_range(0, values.len())])
         }
     }

     /// Shuffle a mutable slice in place.
     fn shuffle<T>(&mut self, values: &mut [T]) {
         let mut i = values.len();
         while i >= 2 {
             // invariant: elements with index >= i have been locked in place.
             i -= 1;
             // lock element i in place.
             values.swap(i, self.gen_range(0, i + 1));
         }
     }
 }

 /// Iterator which will generate a stream of random items.
 ///
 /// This iterator is created via the `gen_iter` method on `Rng`.
 pub struct Generator<'a, T, R: 'a> {
     rng: &'a mut R,
     _marker: PhantomData<T>,
 }

 impl<'a, T: Rand, R: Rng> Iterator for Generator<'a, T, R> {
     type Item = T;

     fn next(&mut self) -> Option<T> {
         Some(self.rng.gen())
     }
 }

 /// Iterator which will continuously generate random ascii characters.
 ///
 /// This iterator is created via the `gen_ascii_chars` method on `Rng`.
 pub struct AsciiGenerator<'a, R: 'a> {
     rng: &'a mut R,
 }

 impl<'a, R: Rng> Iterator for AsciiGenerator<'a, R> {
     type Item = char;

     fn next(&mut self) -> Option<char> {
         const GEN_ASCII_STR_CHARSET: &'static [u8] = b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
                                                        abcdefghijklmnopqrstuvwxyz\
                                                        0123456789";
         Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
     }
 }

 /// A random number generator that can be explicitly seeded to produce
 /// the same stream of randomness multiple times.
 pub trait SeedableRng<Seed>: Rng {
     /// Reseed an RNG with the given seed.
     fn reseed(&mut self, Seed);

     /// Create a new RNG with the given seed.
     fn from_seed(seed: Seed) -> Self;
 }

 /// An Xorshift[1] random number
 /// generator.
 ///
 /// The Xorshift algorithm is not suitable for cryptographic purposes
 /// but is very fast. If you do not know for sure that it fits your
 /// requirements, use a more secure one such as `IsaacRng` or `OsRng`.
 ///
 /// [1]: Marsaglia, George (July 2003). ["Xorshift
 /// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of
 /// Statistical Software*. Vol. 8 (Issue 14).
 #[derive(Clone)]
 pub struct XorShiftRng {
     x: u32,
     y: u32,
     z: u32,
     w: u32,
 }

 impl XorShiftRng {
     /// Creates a new XorShiftRng instance which is not seeded.
     ///
     /// The initial values of this RNG are constants, so all generators created
     /// by this function will yield the same stream of random numbers. It is
     /// highly recommended that this is created through `SeedableRng` instead of
     /// this function
     pub fn new_unseeded() -> XorShiftRng {
         XorShiftRng {
             x: 0x193a6754,
             y: 0xa8a7d469,
             z: 0x97830e05,
             w: 0x113ba7bb,
         }
     }
 }

 impl Rng for XorShiftRng {
     #[inline]
     fn next_u32(&mut self) -> u32 {
         let x = self.x;
         let t = x ^ (x << 11);
         self.x = self.y;
         self.y = self.z;
         self.z = self.w;
         let w = self.w;
         self.w = w ^ (w >> 19) ^ (t ^ (t >> 8));
         self.w
     }
 }

 impl SeedableRng<[u32; 4]> for XorShiftRng {
     /// Reseed an XorShiftRng. This will panic if `seed` is entirely 0.
     fn reseed(&mut self, seed: [u32; 4]) {
         assert!(!seed.iter().all(|&x| x == 0),
                 "XorShiftRng.reseed called with an all zero seed.");

         self.x = seed[0];
         self.y = seed[1];
         self.z = seed[2];
         self.w = seed[3];
     }

     /// Create a new XorShiftRng. This will panic if `seed` is entirely 0.
     fn from_seed(seed: [u32; 4]) -> XorShiftRng {
         assert!(!seed.iter().all(|&x| x == 0),
                 "XorShiftRng::from_seed called with an all zero seed.");

         XorShiftRng {
             x: seed[0],
             y: seed[1],
             z: seed[2],
             w: seed[3],
         }
     }
 }

 impl Rand for XorShiftRng {
     fn rand<R: Rng>(rng: &mut R) -> XorShiftRng {
         let mut tuple: (u32, u32, u32, u32) = rng.gen();
         while tuple == (0, 0, 0, 0) {
             tuple = rng.gen();
         }
         let (x, y, z, w) = tuple;
         XorShiftRng {
             x: x,
             y: y,
             z: z,
             w: w,
         }
     }
 }

 /// A wrapper for generating floating point numbers uniformly in the
 /// open interval `(0,1)` (not including either endpoint).
 ///
 /// Use `Closed01` for the closed interval `[0,1]`, and the default
 /// `Rand` implementation for `f32` and `f64` for the half-open
 /// `[0,1)`.
 pub struct Open01<F>(pub F);

 /// A wrapper for generating floating point numbers uniformly in the
 /// closed interval `[0,1]` (including both endpoints).
 ///
 /// Use `Open01` for the closed interval `(0,1)`, and the default
 /// `Rand` implementation of `f32` and `f64` for the half-open
 /// `[0,1)`.
 pub struct Closed01<F>(pub F);

 #[cfg(test)]
 mod test {
     use std::__rand as rand;

     pub struct MyRng<R> {
         inner: R,
     }

     impl<R: rand::Rng> ::Rng for MyRng<R> {
         fn next_u32(&mut self) -> u32 {
             rand::Rng::next_u32(&mut self.inner)
         }
     }

     pub fn rng() -> MyRng<rand::ThreadRng> {
         MyRng { inner: rand::thread_rng() }
     }

     pub fn weak_rng() -> MyRng<rand::ThreadRng> {
         MyRng { inner: rand::thread_rng() }
     }
 }
	// Copyright 2013-2014 The Rust Project Developers. See the COPYRIGHT
	// file at the top-level directory of this distribution and at
	// http://rust-lang.org/COPYRIGHT.
	//
	// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
	// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
	// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
	// option. This file may not be copied, modified, or distributed
	// except according to those terms.

	//! Interface to random number generators in Rust.
	//!
	//! This is an experimental library which lives underneath the standard library
	//! in its dependency chain. This library is intended to define the interface
	//! for random number generation and also provide utilities around doing so. It
	//! is not recommended to use this library directly, but rather the official
	//! interface through `std::rand`.

	#![crate_name = "rand"]
	#![crate_type = "rlib"]
	#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
	html_favicon_url = "https://doc.rust-lang.org/favicon.ico",
	html_root_url = "https://doc.rust-lang.org/nightly/",
	html_playground_url = "https://play.rust-lang.org/",
	test(attr(deny(warnings))))]
	#![cfg_attr(not(stage0), deny(warnings))]
	#![no_std]
	#![unstable(feature = "rand",
	reason = "use `rand` from crates.io",
	issue = "27703")]
	#![feature(core_intrinsics)]
	#![feature(staged_api)]
	#![feature(step_by)]
	#![feature(custom_attribute)]
	#![allow(unused_attributes)]

	#![cfg_attr(not(test), feature(core_float))] // only necessary for no_std
	#![cfg_attr(test, feature(test, rand))]

	#![allow(deprecated)]

	#[cfg(test)]
	#[macro_use]
	extern crate std;

	use core::f64;
	use core::intrinsics;
	use core::marker::PhantomData;

	pub use isaac::{Isaac64Rng, IsaacRng};
	pub use chacha::ChaChaRng;

	use distributions::{IndependentSample, Range};
	use distributions::range::SampleRange;

	#[cfg(test)]
	const RAND_BENCH_N: u64 = 100;

	pub mod distributions;
	pub mod isaac;
	pub mod chacha;
	pub mod reseeding;
	mod rand_impls;

	// Temporary trait to implement a few floating-point routines
	// needed by librand; this is necessary because librand doesn't
	// depend on libstd. This will go away when librand is integrated
	// into libstd.
	#[doc(hidden)]
	trait FloatMath: Sized {
	fn exp(self) -> Self;
	fn ln(self) -> Self;
	fn sqrt(self) -> Self;
	fn powf(self, n: Self) -> Self;
	}

	impl FloatMath for f64 {
	#[inline]
	fn exp(self) -> f64 {
	unsafe { intrinsics::expf64(self) }
	}

	#[inline]
	fn ln(self) -> f64 {
	unsafe { intrinsics::logf64(self) }
	}

	#[inline]
	fn powf(self, n: f64) -> f64 {
	unsafe { intrinsics::powf64(self, n) }
	}

	#[inline]
	fn sqrt(self) -> f64 {
	if self < 0.0 {
	f64::NAN
	} else {
	unsafe { intrinsics::sqrtf64(self) }
	}
	}
	}

	/// A type that can be randomly generated using an `Rng`.
	#[doc(hidden)]
	pub trait Rand: Sized {
	/// Generates a random instance of this type using the specified source of
	/// randomness.
	fn rand<R: Rng>(rng: &mut R) -> Self;
	}

	/// A random number generator.
	pub trait Rng: Sized {
	/// Return the next random u32.
	///
	/// This rarely needs to be called directly, prefer `r.gen()` to
	/// `r.next_u32()`.
	// FIXME #7771: Should be implemented in terms of next_u64
	fn next_u32(&mut self) -> u32;

	/// Return the next random u64.
	///
	/// By default this is implemented in terms of `next_u32`. An
	/// implementation of this trait must provide at least one of
	/// these two methods. Similarly to `next_u32`, this rarely needs
	/// to be called directly, prefer `r.gen()` to `r.next_u64()`.
	fn next_u64(&mut self) -> u64 {
	((self.next_u32() as u64) << 32) \| (self.next_u32() as u64)
	}

	/// Return the next random f32 selected from the half-open
	/// interval `[0, 1)`.
	///
	/// By default this is implemented in terms of `next_u32`, but a
	/// random number generator which can generate numbers satisfying
	/// the requirements directly can overload this for performance.
	/// It is required that the return value lies in `[0, 1)`.
	///
	/// See `Closed01` for the closed interval `[0,1]`, and
	/// `Open01` for the open interval `(0,1)`.
	fn next_f32(&mut self) -> f32 {
	const MANTISSA_BITS: usize = 24;
	const IGNORED_BITS: usize = 8;
	const SCALE: f32 = (1u64 << MANTISSA_BITS) as f32;

	// using any more than `MANTISSA_BITS` bits will
	// cause (e.g.) 0xffff_ffff to correspond to 1
	// exactly, so we need to drop some (8 for f32, 11
	// for f64) to guarantee the open end.
	(self.next_u32() >> IGNORED_BITS) as f32 / SCALE
	}

	/// Return the next random f64 selected from the half-open
	/// interval `[0, 1)`.
	///
	/// By default this is implemented in terms of `next_u64`, but a
	/// random number generator which can generate numbers satisfying
	/// the requirements directly can overload this for performance.
	/// It is required that the return value lies in `[0, 1)`.
	///
	/// See `Closed01` for the closed interval `[0,1]`, and
	/// `Open01` for the open interval `(0,1)`.
	fn next_f64(&mut self) -> f64 {
	const MANTISSA_BITS: usize = 53;
	const IGNORED_BITS: usize = 11;
	const SCALE: f64 = (1u64 << MANTISSA_BITS) as f64;

	(self.next_u64() >> IGNORED_BITS) as f64 / SCALE
	}

	/// Fill `dest` with random data.
	///
	/// This has a default implementation in terms of `next_u64` and
	/// `next_u32`, but should be overridden by implementations that
	/// offer a more efficient solution than just calling those
	/// methods repeatedly.
	///
	/// This method does not have a requirement to bear any fixed
	/// relationship to the other methods, for example, it does not
	/// have to result in the same output as progressively filling
	/// `dest` with `self.gen::<u8>()`, and any such behaviour should
	/// not be relied upon.
	///
	/// This method should guarantee that `dest` is entirely filled
	/// with new data, and may panic if this is impossible
	/// (e.g. reading past the end of a file that is being used as the
	/// source of randomness).
	fn fill_bytes(&mut self, dest: &mut [u8]) {
	// this could, in theory, be done by transmuting dest to a
	// [u64], but this is (1) likely to be undefined behaviour for
	// LLVM, (2) has to be very careful about alignment concerns,
	// (3) adds more `unsafe` that needs to be checked, (4)
	// probably doesn't give much performance gain if
	// optimisations are on.
	let mut count = 0;
	let mut num = 0;
	for byte in dest {
	if count == 0 {
	// we could micro-optimise here by generating a u32 if
	// we only need a few more bytes to fill the vector
	// (i.e. at most 4).
	num = self.next_u64();
	count = 8;
	}

	*byte = (num & 0xff) as u8;
	num >>= 8;
	count -= 1;
	}
	}

	/// Return a random value of a `Rand` type.
	#[inline(always)]
	fn gen<T: Rand>(&mut self) -> T {
	Rand::rand(self)
	}

	/// Return an iterator that will yield an infinite number of randomly
	/// generated items.
	fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> {
	Generator {
	rng: self,
	_marker: PhantomData,
	}
	}

	/// Generate a random value in the range [`low`, `high`).
	///
	/// This is a convenience wrapper around
	/// `distributions::Range`. If this function will be called
	/// repeatedly with the same arguments, one should use `Range`, as
	/// that will amortize the computations that allow for perfect
	/// uniformity, as they only happen on initialization.
	///
	/// # Panics
	///
	/// Panics if `low >= high`.
	fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T {
	assert!(low < high, "Rng.gen_range called with low >= high");
	Range::new(low, high).ind_sample(self)
	}

	/// Return a bool with a 1 in n chance of true
	fn gen_weighted_bool(&mut self, n: usize) -> bool {
	n <= 1 \|\| self.gen_range(0, n) == 0
	}

	/// Return an iterator of random characters from the set A-Z,a-z,0-9.
	fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> {
	AsciiGenerator { rng: self }
	}

	/// Return a random element from `values`.
	///
	/// Return `None` if `values` is empty.
	fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> {
	if values.is_empty() {
	None
	} else {
	Some(&values[self.gen_range(0, values.len())])
	}
	}

	/// Shuffle a mutable slice in place.
	fn shuffle<T>(&mut self, values: &mut [T]) {
	let mut i = values.len();
	while i >= 2 {
	// invariant: elements with index >= i have been locked in place.
	i -= 1;
	// lock element i in place.
	values.swap(i, self.gen_range(0, i + 1));
	}
	}
	}

	/// Iterator which will generate a stream of random items.
	///
	/// This iterator is created via the `gen_iter` method on `Rng`.
	pub struct Generator<'a, T, R: 'a> {
	rng: &'a mut R,
	_marker: PhantomData<T>,
	}

	impl<'a, T: Rand, R: Rng> Iterator for Generator<'a, T, R> {
	type Item = T;

	fn next(&mut self) -> Option<T> {
	Some(self.rng.gen())
	}
	}

	/// Iterator which will continuously generate random ascii characters.
	///
	/// This iterator is created via the `gen_ascii_chars` method on `Rng`.
	pub struct AsciiGenerator<'a, R: 'a> {
	rng: &'a mut R,
	}

	impl<'a, R: Rng> Iterator for AsciiGenerator<'a, R> {
	type Item = char;

	fn next(&mut self) -> Option<char> {
	const GEN_ASCII_STR_CHARSET: &'static [u8] = b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
	abcdefghijklmnopqrstuvwxyz\
	0123456789";
	Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
	}
	}

	/// A random number generator that can be explicitly seeded to produce
	/// the same stream of randomness multiple times.
	pub trait SeedableRng<Seed>: Rng {
	/// Reseed an RNG with the given seed.
	fn reseed(&mut self, Seed);

	/// Create a new RNG with the given seed.
	fn from_seed(seed: Seed) -> Self;
	}

	/// An Xorshift[1] random number
	/// generator.
	///
	/// The Xorshift algorithm is not suitable for cryptographic purposes
	/// but is very fast. If you do not know for sure that it fits your
	/// requirements, use a more secure one such as `IsaacRng` or `OsRng`.
	///
	/// [1]: Marsaglia, George (July 2003). ["Xorshift
	/// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of
	/// Statistical Software*. Vol. 8 (Issue 14).
	#[derive(Clone)]
	pub struct XorShiftRng {
	x: u32,
	y: u32,
	z: u32,
	w: u32,
	}

	impl XorShiftRng {
	/// Creates a new XorShiftRng instance which is not seeded.
	///
	/// The initial values of this RNG are constants, so all generators created
	/// by this function will yield the same stream of random numbers. It is
	/// highly recommended that this is created through `SeedableRng` instead of
	/// this function
	pub fn new_unseeded() -> XorShiftRng {
	XorShiftRng {
	x: 0x193a6754,
	y: 0xa8a7d469,
	z: 0x97830e05,
	w: 0x113ba7bb,
	}
	}
	}

	impl Rng for XorShiftRng {
	#[inline]
	fn next_u32(&mut self) -> u32 {
	let x = self.x;
	let t = x ^ (x << 11);
	self.x = self.y;
	self.y = self.z;
	self.z = self.w;
	let w = self.w;
	self.w = w ^ (w >> 19) ^ (t ^ (t >> 8));
	self.w
	}
	}

	impl SeedableRng<[u32; 4]> for XorShiftRng {
	/// Reseed an XorShiftRng. This will panic if `seed` is entirely 0.
	fn reseed(&mut self, seed: [u32; 4]) {
	assert!(!seed.iter().all(\|&x\| x == 0),
	"XorShiftRng.reseed called with an all zero seed.");

	self.x = seed[0];
	self.y = seed[1];
	self.z = seed[2];
	self.w = seed[3];
	}

	/// Create a new XorShiftRng. This will panic if `seed` is entirely 0.
	fn from_seed(seed: [u32; 4]) -> XorShiftRng {
	assert!(!seed.iter().all(\|&x\| x == 0),
	"XorShiftRng::from_seed called with an all zero seed.");

	XorShiftRng {
	x: seed[0],
	y: seed[1],
	z: seed[2],
	w: seed[3],
	}
	}
	}

	impl Rand for XorShiftRng {
	fn rand<R: Rng>(rng: &mut R) -> XorShiftRng {
	let mut tuple: (u32, u32, u32, u32) = rng.gen();
	while tuple == (0, 0, 0, 0) {
	tuple = rng.gen();
	}
	let (x, y, z, w) = tuple;
	XorShiftRng {
	x: x,
	y: y,
	z: z,
	w: w,
	}
	}
	}

	/// A wrapper for generating floating point numbers uniformly in the
	/// open interval `(0,1)` (not including either endpoint).
	///
	/// Use `Closed01` for the closed interval `[0,1]`, and the default
	/// `Rand` implementation for `f32` and `f64` for the half-open
	/// `[0,1)`.
	pub struct Open01<F>(pub F);

	/// A wrapper for generating floating point numbers uniformly in the
	/// closed interval `[0,1]` (including both endpoints).
	///
	/// Use `Open01` for the closed interval `(0,1)`, and the default
	/// `Rand` implementation of `f32` and `f64` for the half-open
	/// `[0,1)`.
	pub struct Closed01<F>(pub F);

	#[cfg(test)]
	mod test {
	use std::__rand as rand;

	pub struct MyRng<R> {
	inner: R,
	}

	impl<R: rand::Rng> ::Rng for MyRng<R> {
	fn next_u32(&mut self) -> u32 {
	rand::Rng::next_u32(&mut self.inner)
	}
	}

	pub fn rng() -> MyRng<rand::ThreadRng> {
	MyRng { inner: rand::thread_rng() }
	}

	pub fn weak_rng() -> MyRng<rand::ThreadRng> {
	MyRng { inner: rand::thread_rng() }
	}
	}