lib.rs.html -- source
Description: Source to the Rust file `C:\Users\Gray\.cargo\registry\src\github.com-1ecc6299db9ec823\rand-0.3.18\src\lib.rs`.
Keywords: rust, rustlang, rust-lang

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    // 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.

    //! 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 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::<f64>()`.
    //!
    //! See the `distributions` submodule for sampling random numbers from
    //! distributions like normal and exponential.
    //!
    //! # Usage
    //!
    //! This crate is [on crates.io](https://crates.io/crates/rand) and can be
    //! used by adding `rand` to the dependencies in your project's `Cargo.toml`.
    //!
    //! ```toml
    //! [dependencies]
    //! rand = "0.3"
    //! ```
    //!
    //! and this to your crate root:
    //!
    //! ```rust
    //! extern crate rand;
    //! ```
    //!
    //! # Thread-local RNG
    //!
    //! There is built-in support for a RNG associated with each thread stored
    //! in thread-local storage. This RNG can be accessed via `thread_rng`, or
    //! used implicitly via `random`. This RNG is normally randomly seeded
    //! from an operating-system source of randomness, e.g. `/dev/urandom` on
    //! Unix systems, and will automatically reseed itself from this source
    //! after generating 32 KiB of random data.
    //!
    //! # Cryptographic security
    //!
    //! An application that requires an entropy source for cryptographic purposes
    //! must use `OsRng`, which reads randomness from the source that the operating
    //! system provides (e.g. `/dev/urandom` on Unixes or `CryptGenRandom()` on
    //! Windows).
    //! The other random number generators provided by this module are not suitable
    //! for such purposes.
    //!
    //! *Note*: many Unix systems provide `/dev/random` as well as `/dev/urandom`.
    //! This module uses `/dev/urandom` for the following reasons:
    //!
    //! -   On Linux, `/dev/random` may block if entropy pool is empty;
    //!     `/dev/urandom` will not block.  This does not mean that `/dev/random`
    //!     provides better output than `/dev/urandom`; the kernel internally runs a
    //!     cryptographically secure pseudorandom number generator (CSPRNG) based on
    //!     entropy pool for random number generation, so the "quality" of
    //!     `/dev/random` is not better than `/dev/urandom` in most cases.  However,
    //!     this means that `/dev/urandom` can yield somewhat predictable randomness
    //!     if the entropy pool is very small, such as immediately after first
    //!     booting.  Linux 3.17 added the `getrandom(2)` system call which solves
    //!     the issue: it blocks if entropy pool is not initialized yet, but it does
    //!     not block once initialized.  `OsRng` tries to use `getrandom(2)` if
    //!     available, and use `/dev/urandom` fallback if not.  If an application
    //!     does not have `getrandom` and likely to be run soon after first booting,
    //!     or on a system with very few entropy sources, one should consider using
    //!     `/dev/random` via `ReadRng`.
    //! -   On some systems (e.g. FreeBSD, OpenBSD and Mac OS X) there is no
    //!     difference between the two sources. (Also note that, on some systems
    //!     e.g.  FreeBSD, both `/dev/random` and `/dev/urandom` may block once if
    //!     the CSPRNG has not seeded yet.)
    //!
    //! # Examples
    //!
    //! ```rust
    //! use rand::Rng;
    //!
    //! let mut rng = rand::thread_rng();
    //! if rng.gen() { // random bool
    //!     println!("i32: {}, u32: {}", rng.gen::<i32>(), rng.gen::<u32>())
    //! }
    //! ```
    //!
    //! ```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
    //! circle, both centered at the origin. Since the area of a unit circle is π,
    //! we have:
    //!
    //! ```text
    //!     (area of unit circle) / (area of square) = π / 4
    //! ```
    //!
    //! So if we sample many points randomly from the square, roughly π / 4 of them
    //! should be inside the circle.
    //!
    //! We can use the above fact to estimate the value of π: pick many points in
    //! the square at random, calculate the fraction that fall within the circle,
    //! and multiply this fraction by 4.
    //!
    //! ```
    //! use rand::distributions::{IndependentSample, Range};
    //!
    //! fn main() {
    //!    let between = Range::new(-1f64, 1.);
    //!    let mut rng = rand::thread_rng();
    //!
    //!    let total = 1_000_000;
    //!    let mut in_circle = 0;
    //!
    //!    for _ in 0..total {
    //!        let a = between.ind_sample(&mut rng);
    //!        let b = between.ind_sample(&mut rng);
    //!        if a*a + b*b <= 1. {
    //!            in_circle += 1;
    //!        }
    //!    }
    //!
    //!    // prints something close to 3.14159...
    //!    println!("{}", 4. * (in_circle as f64) / (total as f64));
    //! }
    //! ```
    //!
    //! ## Monty Hall Problem
    //!
    //! This is a simulation of the [Monty Hall Problem][]:
    //!
    //! > Suppose you're on a game show, and you're given the choice of three doors:
    //! > Behind one door is a car; behind the others, goats. You pick a door, say
    //! > No. 1, and the host, who knows what's behind the doors, opens another
    //! > door, say No. 3, which has a goat. He then says to you, "Do you want to
    //! > pick door No. 2?" Is it to your advantage to switch your choice?
    //!
    //! The rather unintuitive answer is that you will have a 2/3 chance of winning
    //! if you switch and a 1/3 chance of winning if you don't, so it's better to
    //! switch.
    //!
    //! This program will simulate the game show and with large enough simulation
    //! steps it will indeed confirm that it is better to switch.
    //!
    //! [Monty Hall Problem]: http://en.wikipedia.org/wiki/Monty_Hall_problem
    //!
    //! ```
    //! use rand::Rng;
    //! use rand::distributions::{IndependentSample, Range};
    //!
    //! struct SimulationResult {
    //!     win: bool,
    //!     switch: bool,
    //! }
    //!
    //! // Run a single simulation of the Monty Hall problem.
    //! fn simulate<R: Rng>(random_door: &Range<u32>, rng: &mut R)
    //!                     -> SimulationResult {
    //!     let car = random_door.ind_sample(rng);
    //!
    //!     // This is our initial choice
    //!     let mut choice = random_door.ind_sample(rng);
    //!
    //!     // The game host opens a door
    //!     let open = game_host_open(car, choice, rng);
    //!
    //!     // Shall we switch?
    //!     let switch = rng.gen();
    //!     if switch {
    //!         choice = switch_door(choice, open);
    //!     }
    //!
    //!     SimulationResult { win: choice == car, switch: switch }
    //! }
    //!
    //! // Returns the door the game host opens given our choice and knowledge of
    //! // where the car is. The game host will never open the door with the car.
    //! fn game_host_open<R: Rng>(car: u32, choice: u32, rng: &mut R) -> u32 {
    //!     let choices = free_doors(&[car, choice]);
    //!     rand::sample(rng, choices.into_iter(), 1)[0]
    //! }
    //!
    //! // Returns the door we switch to, given our current choice and
    //! // the open door. There will only be one valid door.
    //! fn switch_door(choice: u32, open: u32) -> u32 {
    //!     free_doors(&[choice, open])[0]
    //! }
    //!
    //! fn free_doors(blocked: &[u32]) -> Vec<u32> {
    //!     (0..3).filter(|x| !blocked.contains(x)).collect()
    //! }
    //!
    //! fn main() {
    //!     // The estimation will be more accurate with more simulations
    //!     let num_simulations = 10000;
    //!
    //!     let mut rng = rand::thread_rng();
    //!     let random_door = Range::new(0, 3);
    //!
    //!     let (mut switch_wins, mut switch_losses) = (0, 0);
    //!     let (mut keep_wins, mut keep_losses) = (0, 0);
    //!
    //!     println!("Running {} simulations...", num_simulations);
    //!     for _ in 0..num_simulations {
    //!         let result = simulate(&random_door, &mut rng);
    //!
    //!         match (result.win, result.switch) {
    //!             (true, true) => switch_wins += 1,
    //!             (true, false) => keep_wins += 1,
    //!             (false, true) => switch_losses += 1,
    //!             (false, false) => keep_losses += 1,
    //!         }
    //!     }
    //!
    //!     let total_switches = switch_wins + switch_losses;
    //!     let total_keeps = keep_wins + keep_losses;
    //!
    //!     println!("Switched door {} times with {} wins and {} losses",
    //!              total_switches, switch_wins, switch_losses);
    //!
    //!     println!("Kept our choice {} times with {} wins and {} losses",
    //!              total_keeps, keep_wins, keep_losses);
    //!
    //!     // With a large number of simulations, the values should converge to
    //!     // 0.667 and 0.333 respectively.
    //!     println!("Estimated chance to win if we switch: {}",
    //!              switch_wins as f32 / total_switches as f32);
    //!     println!("Estimated chance to win if we don't: {}",
    //!              keep_wins as f32 / total_keeps as f32);
    //! }
    //! ```

    #![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
           html_favicon_url = "https://www.rust-lang.org/favicon.ico",
           html_root_url = "https://docs.rs/rand/0.3")]

    #![deny(missing_debug_implementations)]

    #![cfg_attr(feature = "i128_support", feature(i128_type))]

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

    use std::cell::RefCell;
    use std::marker;
    use std::mem;
    use std::io;
    use std::rc::Rc;
    use std::num::Wrapping as w;
    use std::time;

    pub use os::OsRng;

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

    #[cfg(target_pointer_width = "32")]
    use IsaacRng as IsaacWordRng;
    #[cfg(target_pointer_width = "64")]
    use Isaac64Rng as IsaacWordRng;

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

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

    #[allow(bad_style)]
    type w64 = w<u64>;
    #[allow(bad_style)]
    type w32 = w<u32>;

    /// A type that can be randomly generated using an `Rng`.
    ///
    /// ## Built-in Implementations
    ///
    /// This crate implements `Rand` for various primitive types.  Assuming the
    /// provided `Rng` is well-behaved, these implementations generate values with
    /// the following ranges and distributions:
    ///
    /// * Integers (`i32`, `u32`, `isize`, `usize`, etc.): Uniformly distributed
    ///   over all values of the type.
    /// * `char`: Uniformly distributed over all Unicode scalar values, i.e. all
    ///   code points in the range `0...0x10_FFFF`, except for the range
    ///   `0xD800...0xDFFF` (the surrogate code points).  This includes
    ///   unassigned/reserved code points.
    /// * `bool`: Generates `false` or `true`, each with probability 0.5.
    /// * Floating point types (`f32` and `f64`): Uniformly distributed in the
    ///   half-open range `[0, 1)`.  (The [`Open01`], [`Closed01`], [`Exp1`], and
    ///   [`StandardNormal`] wrapper types produce floating point numbers with
    ///   alternative ranges or distributions.)
    ///
    /// [`Open01`]: struct.Open01.html
    /// [`Closed01`]: struct.Closed01.html
    /// [`Exp1`]: struct.Exp1.html
    /// [`StandardNormal`]: struct.StandardNormal.html
    ///
    /// The following aggregate types also implement `Rand` as long as their
    /// component types implement it:
    ///
    /// * Tuples and arrays: Each element of the tuple or array is generated
    ///   independently, using its own `Rand` implementation.
    /// * `Option<T>`: Returns `None` with probability 0.5; otherwise generates a
    ///   random `T` and returns `Some(T)`.

    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 {
        /// Return the next random u32.
        ///
        /// This rarely needs to be called directly, prefer `r.gen()` to
        /// `r.next_u32()`.
        // FIXME #rust-lang/rfcs#628: 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)`.
        ///
        /// This uses a technique described by Saito and Matsumoto at
        /// MCQMC'08. Given that the IEEE floating point numbers are
        /// uniformly distributed over [1,2), we generate a number in
        /// this range and then offset it onto the range [0,1). Our
        /// choice of bits (masking v. shifting) is arbitrary and
        /// should be immaterial for high quality generators. For low
        /// quality generators (ex. LCG), prefer bitshifting due to
        /// correlation between sequential low order bits.
        ///
        /// See:
        /// A PRNG specialized in double precision floating point numbers using
        /// an affine transition
        ///
        /// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/ARTICLES/dSFMT.pdf>
        /// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/dSFMT-slide-e.pdf>
        ///
        /// 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 UPPER_MASK: u32 = 0x3F800000;
            const LOWER_MASK: u32 = 0x7FFFFF;
            let tmp = UPPER_MASK | (self.next_u32() & LOWER_MASK);
            let result: f32 = unsafe { mem::transmute(tmp) };
            result - 1.0
        }

        /// 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 UPPER_MASK: u64 = 0x3FF0000000000000;
            const LOWER_MASK: u64 = 0xFFFFFFFFFFFFF;
            let tmp = UPPER_MASK | (self.next_u64() & LOWER_MASK);
            let result: f64 = unsafe { mem::transmute(tmp) };
            result - 1.0
        }

        /// 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).
        ///
        /// # Example
        ///
        /// ```rust
        /// use rand::{thread_rng, Rng};
        ///
        /// let mut v = [0u8; 13579];
        /// thread_rng().fill_bytes(&mut v);
        /// println!("{:?}", &v[..]);
        /// ```
        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.iter_mut() {
                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.
        ///
        /// # Example
        ///
        /// ```rust
        /// use rand::{thread_rng, Rng};
        ///
        /// let mut rng = thread_rng();
        /// let x: u32 = rng.gen();
        /// println!("{}", x);
        /// println!("{:?}", rng.gen::<(f64, bool)>());
        /// ```
        #[inline(always)]
        fn gen<T: Rand>(&mut self) -> T where Self: Sized {
            Rand::rand(self)
        }

        /// Return an iterator that will yield an infinite number of randomly
        /// generated items.
        ///
        /// # Example
        ///
        /// ```
        /// use rand::{thread_rng, Rng};
        ///
        /// let mut rng = thread_rng();
        /// let x = rng.gen_iter::<u32>().take(10).collect::<Vec<u32>>();
        /// println!("{:?}", x);
        /// println!("{:?}", rng.gen_iter::<(f64, bool)>().take(5)
        ///                     .collect::<Vec<(f64, bool)>>());
        /// ```
        fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> where Self: Sized {
            Generator { rng: self, _marker: 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`.
        ///
        /// # Example
        ///
        /// ```rust
        /// use rand::{thread_rng, Rng};
        ///
        /// let mut rng = thread_rng();
        /// let n: u32 = rng.gen_range(0, 10);
        /// println!("{}", n);
        /// let m: f64 = rng.gen_range(-40.0f64, 1.3e5f64);
        /// println!("{}", m);
        /// ```
        fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T where Self: Sized {
            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
        ///
        /// # Example
        ///
        /// ```rust
        /// use rand::{thread_rng, Rng};
        ///
        /// let mut rng = thread_rng();
        /// println!("{}", rng.gen_weighted_bool(3));
        /// ```
        fn gen_weighted_bool(&mut self, n: u32) -> bool where Self: Sized {
            n <= 1 || self.gen_range(0, n) == 0
        }

        /// Return an iterator of random characters from the set A-Z,a-z,0-9.
        ///
        /// # Example
        ///
        /// ```rust
        /// use rand::{thread_rng, Rng};
        ///
        /// let s: String = thread_rng().gen_ascii_chars().take(10).collect();
        /// println!("{}", s);
        /// ```
        fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> where Self: Sized {
            AsciiGenerator { rng: self }
        }

        /// Return a random element from `values`.
        ///
        /// Return `None` if `values` is empty.
        ///
        /// # Example
        ///
        /// ```
        /// use rand::{thread_rng, Rng};
        ///
        /// let choices = [1, 2, 4, 8, 16, 32];
        /// let mut rng = thread_rng();
        /// println!("{:?}", rng.choose(&choices));
        /// assert_eq!(rng.choose(&choices[..0]), None);
        /// ```
        fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> where Self: Sized {
            if values.is_empty() {
                None
            } else {
                Some(&values[self.gen_range(0, values.len())])
            }
        }

        /// Return a mutable pointer to a random element from `values`.
        ///
        /// Return `None` if `values` is empty.
        fn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T> where Self: Sized {
            if values.is_empty() {
                None
            } else {
                let len = values.len();
                Some(&mut values[self.gen_range(0, len)])
            }
        }

        /// Shuffle a mutable slice in place.
        ///
        /// This applies Durstenfeld's algorithm for the [Fisher–Yates shuffle](https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm)
        /// which produces an unbiased permutation.
        ///
        /// # Example
        ///
        /// ```rust
        /// use rand::{thread_rng, Rng};
        ///
        /// let mut rng = thread_rng();
        /// let mut y = [1, 2, 3];
        /// rng.shuffle(&mut y);
        /// println!("{:?}", y);
        /// rng.shuffle(&mut y);
        /// println!("{:?}", y);
        /// ```
        fn shuffle<T>(&mut self, values: &mut [T]) where Self: Sized {
            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));
            }
        }
    }

    impl<'a, R: ?Sized> Rng for &'a mut R where R: Rng {
        fn next_u32(&mut self) -> u32 {
            (**self).next_u32()
        }

        fn next_u64(&mut self) -> u64 {
            (**self).next_u64()
        }

        fn next_f32(&mut self) -> f32 {
            (**self).next_f32()
        }

        fn next_f64(&mut self) -> f64 {
            (**self).next_f64()
        }

        fn fill_bytes(&mut self, dest: &mut [u8]) {
            (**self).fill_bytes(dest)
        }
    }

    impl<R: ?Sized> Rng for Box<R> where R: Rng {
        fn next_u32(&mut self) -> u32 {
            (**self).next_u32()
        }

        fn next_u64(&mut self) -> u64 {
            (**self).next_u64()
        }

        fn next_f32(&mut self) -> f32 {
            (**self).next_f32()
        }

        fn next_f64(&mut self) -> f64 {
            (**self).next_f64()
        }

        fn fill_bytes(&mut self, dest: &mut [u8]) {
            (**self).fill_bytes(dest)
        }
    }

    /// Iterator which will generate a stream of random items.
    ///
    /// This iterator is created via the [`gen_iter`] method on [`Rng`].
    ///
    /// [`gen_iter`]: trait.Rng.html#method.gen_iter
    /// [`Rng`]: trait.Rng.html
    #[derive(Debug)]
    pub struct Generator<'a, T, R:'a> {
        rng: &'a mut R,
        _marker: marker::PhantomData<fn() -> 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`].
    ///
    /// [`gen_ascii_chars`]: trait.Rng.html#method.gen_ascii_chars
    /// [`Rng`]: trait.Rng.html
    #[derive(Debug)]
    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.
        ///
        /// # Example
        ///
        /// ```rust
        /// use rand::{Rng, SeedableRng, StdRng};
        ///
        /// let seed: &[_] = &[1, 2, 3, 4];
        /// let mut rng: StdRng = SeedableRng::from_seed(seed);
        /// println!("{}", rng.gen::<f64>());
        /// rng.reseed(&[5, 6, 7, 8]);
        /// println!("{}", rng.gen::<f64>());
        /// ```
        fn reseed(&mut self, Seed);

        /// Create a new RNG with the given seed.
        ///
        /// # Example
        ///
        /// ```rust
        /// use rand::{Rng, SeedableRng, StdRng};
        ///
        /// let seed: &[_] = &[1, 2, 3, 4];
        /// let mut rng: StdRng = SeedableRng::from_seed(seed);
        /// println!("{}", rng.gen::<f64>());
        /// ```
        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).
    #[allow(missing_copy_implementations)]
    #[derive(Clone, Debug)]
    pub struct XorShiftRng {
        x: w32,
        y: w32,
        z: w32,
        w: w32,
    }

    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: w(0x193a6754),
                y: w(0xa8a7d469),
                z: w(0x97830e05),
                w: 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.0
        }
    }

    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 = w(seed[0]);
            self.y = w(seed[1]);
            self.z = w(seed[2]);
            self.w = 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: w(seed[0]),
                y: w(seed[1]),
                z: w(seed[2]),
                w: 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: w(x), y: w(y), z: w(z), w: 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)`.
    ///
    /// # Example
    /// ```rust
    /// use rand::{random, Open01};
    ///
    /// let Open01(val) = random::<Open01<f32>>();
    /// println!("f32 from (0,1): {}", val);
    /// ```
    #[derive(Debug)]
    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)`.
    ///
    /// # Example
    ///
    /// ```rust
    /// use rand::{random, Closed01};
    ///
    /// let Closed01(val) = random::<Closed01<f32>>();
    /// println!("f32 from [0,1]: {}", val);
    /// ```
    #[derive(Debug)]
    pub struct Closed01<F>(pub F);

    /// The standard RNG. This is designed to be efficient on the current
    /// platform.
    #[derive(Copy, Clone, Debug)]
    pub struct StdRng {
        rng: IsaacWordRng,
    }

    impl StdRng {
        /// Create a randomly seeded instance of `StdRng`.
        ///
        /// This is a very expensive operation as it has to read
        /// randomness from the operating system and use this in an
        /// expensive seeding operation. If one is only generating a small
        /// number of random numbers, or doesn't need the utmost speed for
        /// generating each number, `thread_rng` and/or `random` may be more
        /// appropriate.
        ///
        /// Reading the randomness from the OS may fail, and any error is
        /// propagated via the `io::Result` return value.
        pub fn new() -> io::Result<StdRng> {
            OsRng::new().map(|mut r| StdRng { rng: r.gen() })
        }
    }

    impl Rng for StdRng {
        #[inline]
        fn next_u32(&mut self) -> u32 {
            self.rng.next_u32()
        }

        #[inline]
        fn next_u64(&mut self) -> u64 {
            self.rng.next_u64()
        }
    }

    impl<'a> SeedableRng<&'a [usize]> for StdRng {
        fn reseed(&mut self, seed: &'a [usize]) {
            // the internal RNG can just be seeded from the above
            // randomness.
            self.rng.reseed(unsafe {mem::transmute(seed)})
        }

        fn from_seed(seed: &'a [usize]) -> StdRng {
            StdRng { rng: SeedableRng::from_seed(unsafe {mem::transmute(seed)}) }
        }
    }

    /// Create a weak random number generator with a default algorithm and seed.
    ///
    /// It returns the fastest `Rng` algorithm currently available in Rust without
    /// consideration for cryptography or security. If you require a specifically
    /// seeded `Rng` for consistency over time you should pick one algorithm and
    /// create the `Rng` yourself.
    ///
    /// This will seed the generator with randomness from thread_rng.
    pub fn weak_rng() -> XorShiftRng {
        thread_rng().gen()
    }

    /// Controls how the thread-local RNG is reseeded.
    #[derive(Debug)]
    struct ThreadRngReseeder;

    impl reseeding::Reseeder<StdRng> for ThreadRngReseeder {
        fn reseed(&mut self, rng: &mut StdRng) {
            match StdRng::new() {
                Ok(r) => *rng = r,
                Err(_) => rng.reseed(&weak_seed())
            }
        }
    }
    const THREAD_RNG_RESEED_THRESHOLD: u64 = 32_768;
    type ThreadRngInner = reseeding::ReseedingRng<StdRng, ThreadRngReseeder>;

    /// The thread-local RNG.
    #[derive(Clone, Debug)]
    pub struct ThreadRng {
        rng: Rc<RefCell<ThreadRngInner>>,
    }

    /// Retrieve the lazily-initialized thread-local random number
    /// generator, seeded by the system. Intended to be used in method
    /// chaining style, e.g. `thread_rng().gen::<i32>()`.
    ///
    /// After generating a certain amount of randomness, the RNG will reseed itself
    /// from the operating system or, if the operating system RNG returns an error,
    /// a seed based on the current system time.
    ///
    /// The internal RNG used is platform and architecture dependent, even
    /// if the operating system random number generator is rigged to give
    /// the same sequence always. If absolute consistency is required,
    /// explicitly select an RNG, e.g. `IsaacRng` or `Isaac64Rng`.
    pub fn thread_rng() -> ThreadRng {
        // used to make space in TLS for a random number generator
        thread_local!(static THREAD_RNG_KEY: Rc<RefCell<ThreadRngInner>> = {
            let r = match StdRng::new() {
                Ok(r) => r,
                Err(_) => StdRng::from_seed(&weak_seed())
            };
            let rng = reseeding::ReseedingRng::new(r,
                                                   THREAD_RNG_RESEED_THRESHOLD,
                                                   ThreadRngReseeder);
            Rc::new(RefCell::new(rng))
        });

        ThreadRng { rng: THREAD_RNG_KEY.with(|t| t.clone()) }
    }

    fn weak_seed() -> [usize; 2] {
        let now = time::SystemTime::now();
        let unix_time = now.duration_since(time::UNIX_EPOCH).unwrap();
        let seconds = unix_time.as_secs() as usize;
        let nanoseconds = unix_time.subsec_nanos() as usize;
        [seconds, nanoseconds]
    }

    impl Rng for ThreadRng {
        fn next_u32(&mut self) -> u32 {
            self.rng.borrow_mut().next_u32()
        }

        fn next_u64(&mut self) -> u64 {
            self.rng.borrow_mut().next_u64()
        }

        #[inline]
        fn fill_bytes(&mut self, bytes: &mut [u8]) {
            self.rng.borrow_mut().fill_bytes(bytes)
        }
    }

    /// Generates a random value using the thread-local random number generator.
    ///
    /// `random()` can generate various types of random things, and so may require
    /// type hinting to generate the specific type you want.
    ///
    /// This function uses the thread local random number generator. This means
    /// that if you're calling `random()` in a loop, caching the generator can
    /// increase performance. An example is shown below.
    ///
    /// # 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!");
    /// }
    /// ```
    ///
    /// Caching the thread local random number generator:
    ///
    /// ```
    /// use rand::Rng;
    ///
    /// let mut v = vec![1, 2, 3];
    ///
    /// for x in v.iter_mut() {
    ///     *x = rand::random()
    /// }
    ///
    /// // would be faster as
    ///
    /// let mut rng = rand::thread_rng();
    ///
    /// for x in v.iter_mut() {
    ///     *x = rng.gen();
    /// }
    /// ```
    #[inline]
    pub fn random<T: Rand>() -> T {
        thread_rng().gen()
    }

    /// Randomly sample up to `amount` elements from a finite iterator.
    /// The order of elements in the sample is not random.
    ///
    /// # Example
    ///
    /// ```rust
    /// use rand::{thread_rng, sample};
    ///
    /// let mut rng = thread_rng();
    /// let sample = sample(&mut rng, 1..100, 5);
    /// println!("{:?}", sample);
    /// ```
    pub fn sample<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Vec<T>
        where I: IntoIterator<Item=T>,
              R: Rng,
    {
        let mut iter = iterable.into_iter();
        let mut reservoir: Vec<T> = iter.by_ref().take(amount).collect();
        // continue unless the iterator was exhausted
        if reservoir.len() == amount {
            for (i, elem) in iter.enumerate() {
                let k = rng.gen_range(0, i + 1 + amount);
                if let Some(spot) = reservoir.get_mut(k) {
                    *spot = elem;
                }
            }
        }
        reservoir
    }

    #[cfg(test)]
    mod test {
        use super::{Rng, thread_rng, random, SeedableRng, StdRng, sample,
                    weak_rng};
        use std::iter::repeat;

        pub struct MyRng<R> { inner: R }

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

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

        struct ConstRng { i: u64 }
        impl Rng for ConstRng {
            fn next_u32(&mut self) -> u32 { self.i as u32 }
            fn next_u64(&mut self) -> u64 { self.i }

            // no fill_bytes on purpose
        }

        pub fn iter_eq<I, J>(i: I, j: J) -> bool
            where I: IntoIterator,
                  J: IntoIterator<Item=I::Item>,
                  I::Item: Eq
        {
            // make sure the iterators have equal length
            let mut i = i.into_iter();
            let mut j = j.into_iter();
            loop {
                match (i.next(), j.next()) {
                    (Some(ref ei), Some(ref ej)) if ei == ej => { }
                    (None, None) => return true,
                    _ => return false,
                }
            }
        }

        #[test]
        fn test_fill_bytes_default() {
            let mut r = ConstRng { i: 0x11_22_33_44_55_66_77_88 };

            // check every remainder mod 8, both in small and big vectors.
            let lengths = [0, 1, 2, 3, 4, 5, 6, 7,
                           80, 81, 82, 83, 84, 85, 86, 87];
            for &n in lengths.iter() {
                let mut v = repeat(0u8).take(n).collect::<Vec<_>>();
                r.fill_bytes(&mut v);

                // use this to get nicer error messages.
                for (i, &byte) in v.iter().enumerate() {
                    if byte == 0 {
                        panic!("byte {} of {} is zero", i, n)
                    }
                }
            }
        }

        #[test]
        fn test_gen_range() {
            let mut r = thread_rng();
            for _ in 0..1000 {
                let a = r.gen_range(-3, 42);
                assert!(a >= -3 && a < 42);
                assert_eq!(r.gen_range(0, 1), 0);
                assert_eq!(r.gen_range(-12, -11), -12);
            }

            for _ in 0..1000 {
                let a = r.gen_range(10, 42);
                assert!(a >= 10 && a < 42);
                assert_eq!(r.gen_range(0, 1), 0);
                assert_eq!(r.gen_range(3_000_000, 3_000_001), 3_000_000);
            }

        }

        #[test]
        #[should_panic]
        fn test_gen_range_panic_int() {
            let mut r = thread_rng();
            r.gen_range(5, -2);
        }

        #[test]
        #[should_panic]
        fn test_gen_range_panic_usize() {
            let mut r = thread_rng();
            r.gen_range(5, 2);
        }

        #[test]
        fn test_gen_f64() {
            let mut r = thread_rng();
            let a = r.gen::<f64>();
            let b = r.gen::<f64>();
            debug!("{:?}", (a, b));
        }

        #[test]
        fn test_gen_weighted_bool() {
            let mut r = thread_rng();
            assert_eq!(r.gen_weighted_bool(0), true);
            assert_eq!(r.gen_weighted_bool(1), true);
        }

        #[test]
        fn test_gen_ascii_str() {
            let mut r = thread_rng();
            assert_eq!(r.gen_ascii_chars().take(0).count(), 0);
            assert_eq!(r.gen_ascii_chars().take(10).count(), 10);
            assert_eq!(r.gen_ascii_chars().take(16).count(), 16);
        }

        #[test]
        fn test_gen_vec() {
            let mut r = thread_rng();
            assert_eq!(r.gen_iter::<u8>().take(0).count(), 0);
            assert_eq!(r.gen_iter::<u8>().take(10).count(), 10);
            assert_eq!(r.gen_iter::<f64>().take(16).count(), 16);
        }

        #[test]
        fn test_choose() {
            let mut r = thread_rng();
            assert_eq!(r.choose(&[1, 1, 1]).map(|&x|x), Some(1));

            let v: &[isize] = &[];
            assert_eq!(r.choose(v), None);
        }

        #[test]
        fn test_shuffle() {
            let mut r = thread_rng();
            let empty: &mut [isize] = &mut [];
            r.shuffle(empty);
            let mut one = [1];
            r.shuffle(&mut one);
            let b: &[_] = &[1];
            assert_eq!(one, b);

            let mut two = [1, 2];
            r.shuffle(&mut two);
            assert!(two == [1, 2] || two == [2, 1]);

            let mut x = [1, 1, 1];
            r.shuffle(&mut x);
            let b: &[_] = &[1, 1, 1];
            assert_eq!(x, b);
        }

        #[test]
        fn test_thread_rng() {
            let mut r = thread_rng();
            r.gen::<i32>();
            let mut v = [1, 1, 1];
            r.shuffle(&mut v);
            let b: &[_] = &[1, 1, 1];
            assert_eq!(v, b);
            assert_eq!(r.gen_range(0, 1), 0);
        }

        #[test]
        fn test_rng_trait_object() {
            let mut rng = thread_rng();
            {
                let mut r = &mut rng as &mut Rng;
                r.next_u32();
                (&mut r).gen::<i32>();
                let mut v = [1, 1, 1];
                (&mut r).shuffle(&mut v);
                let b: &[_] = &[1, 1, 1];
                assert_eq!(v, b);
                assert_eq!((&mut r).gen_range(0, 1), 0);
            }
            {
                let mut r = Box::new(rng) as Box<Rng>;
                r.next_u32();
                r.gen::<i32>();
                let mut v = [1, 1, 1];
                r.shuffle(&mut v);
                let b: &[_] = &[1, 1, 1];
                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();
        }

        #[test]
        fn test_sample() {
            let min_val = 1;
            let max_val = 100;

            let mut r = thread_rng();
            let vals = (min_val..max_val).collect::<Vec<i32>>();
            let small_sample = sample(&mut r, vals.iter(), 5);
            let large_sample = sample(&mut r, vals.iter(), vals.len() + 5);

            assert_eq!(small_sample.len(), 5);
            assert_eq!(large_sample.len(), vals.len());

            assert!(small_sample.iter().all(|e| {
                **e >= min_val && **e <= max_val
            }));
        }

        #[test]
        fn test_std_rng_seeded() {
            let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>();
            let mut ra: StdRng = SeedableRng::from_seed(&s[..]);
            let mut rb: StdRng = SeedableRng::from_seed(&s[..]);
            assert!(iter_eq(ra.gen_ascii_chars().take(100),
                            rb.gen_ascii_chars().take(100)));
        }

        #[test]
        fn test_std_rng_reseed() {
            let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>();
            let mut r: StdRng = SeedableRng::from_seed(&s[..]);
            let string1 = r.gen_ascii_chars().take(100).collect::<String>();

            r.reseed(&s);

            let string2 = r.gen_ascii_chars().take(100).collect::<String>();
            assert_eq!(string1, string2);
        }

        #[test]
        fn test_weak_rng() {
            let s = weak_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>();
            let mut ra: StdRng = SeedableRng::from_seed(&s[..]);
            let mut rb: StdRng = SeedableRng::from_seed(&s[..]);
            assert!(iter_eq(ra.gen_ascii_chars().take(100),
                            rb.gen_ascii_chars().take(100)));
        }
    }

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