448 lines
14 KiB
Rust
448 lines
14 KiB
Rust
// Copyright 2013-2014 The Rust Project Developers. See the COPYRIGHT
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// file at the top-level directory of this distribution and at
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// http://rust-lang.org/COPYRIGHT.
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//
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// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
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// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
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// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
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// option. This file may not be copied, modified, or distributed
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// except according to those terms.
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//! Interface to random number generators in Rust.
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//!
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//! This is an experimental library which lives underneath the standard library
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//! in its dependency chain. This library is intended to define the interface
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//! for random number generation and also provide utilities around doing so. It
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//! is not recommended to use this library directly, but rather the official
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//! interface through `std::rand`.
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#![crate_name = "rand"]
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#![crate_type = "rlib"]
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#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
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html_favicon_url = "https://doc.rust-lang.org/favicon.ico",
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html_root_url = "https://doc.rust-lang.org/nightly/",
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html_playground_url = "https://play.rust-lang.org/",
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test(attr(deny(warnings))))]
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#![cfg_attr(not(stage0), deny(warnings))]
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#![no_std]
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#![unstable(feature = "rand",
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reason = "use `rand` from crates.io",
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issue = "27703")]
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#![feature(core_intrinsics)]
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#![feature(staged_api)]
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#![feature(step_by)]
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#![feature(custom_attribute)]
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#![allow(unused_attributes)]
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#![cfg_attr(not(test), feature(core_float))] // only necessary for no_std
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#![cfg_attr(test, feature(test, rand))]
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#![allow(deprecated)]
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#[cfg(test)]
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#[macro_use]
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extern crate std;
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use core::f64;
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use core::intrinsics;
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use core::marker::PhantomData;
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pub use isaac::{Isaac64Rng, IsaacRng};
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pub use chacha::ChaChaRng;
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use distributions::{IndependentSample, Range};
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use distributions::range::SampleRange;
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#[cfg(test)]
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const RAND_BENCH_N: u64 = 100;
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pub mod distributions;
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pub mod isaac;
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pub mod chacha;
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pub mod reseeding;
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mod rand_impls;
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// Temporary trait to implement a few floating-point routines
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// needed by librand; this is necessary because librand doesn't
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// depend on libstd. This will go away when librand is integrated
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// into libstd.
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#[doc(hidden)]
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trait FloatMath: Sized {
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fn exp(self) -> Self;
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fn ln(self) -> Self;
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fn sqrt(self) -> Self;
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fn powf(self, n: Self) -> Self;
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}
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impl FloatMath for f64 {
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#[inline]
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fn exp(self) -> f64 {
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unsafe { intrinsics::expf64(self) }
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}
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#[inline]
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fn ln(self) -> f64 {
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unsafe { intrinsics::logf64(self) }
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}
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#[inline]
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fn powf(self, n: f64) -> f64 {
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unsafe { intrinsics::powf64(self, n) }
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}
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#[inline]
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fn sqrt(self) -> f64 {
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if self < 0.0 {
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f64::NAN
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} else {
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unsafe { intrinsics::sqrtf64(self) }
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}
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}
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}
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/// A type that can be randomly generated using an `Rng`.
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#[doc(hidden)]
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pub trait Rand: Sized {
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/// Generates a random instance of this type using the specified source of
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/// randomness.
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fn rand<R: Rng>(rng: &mut R) -> Self;
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}
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/// A random number generator.
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pub trait Rng: Sized {
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/// Return the next random u32.
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///
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/// This rarely needs to be called directly, prefer `r.gen()` to
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/// `r.next_u32()`.
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// FIXME #7771: Should be implemented in terms of next_u64
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fn next_u32(&mut self) -> u32;
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/// Return the next random u64.
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///
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/// By default this is implemented in terms of `next_u32`. An
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/// implementation of this trait must provide at least one of
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/// these two methods. Similarly to `next_u32`, this rarely needs
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/// to be called directly, prefer `r.gen()` to `r.next_u64()`.
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fn next_u64(&mut self) -> u64 {
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((self.next_u32() as u64) << 32) | (self.next_u32() as u64)
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}
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/// Return the next random f32 selected from the half-open
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/// interval `[0, 1)`.
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///
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/// By default this is implemented in terms of `next_u32`, but a
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/// random number generator which can generate numbers satisfying
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/// the requirements directly can overload this for performance.
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/// It is required that the return value lies in `[0, 1)`.
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///
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/// See `Closed01` for the closed interval `[0,1]`, and
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/// `Open01` for the open interval `(0,1)`.
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fn next_f32(&mut self) -> f32 {
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const MANTISSA_BITS: usize = 24;
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const IGNORED_BITS: usize = 8;
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const SCALE: f32 = (1u64 << MANTISSA_BITS) as f32;
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// using any more than `MANTISSA_BITS` bits will
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// cause (e.g.) 0xffff_ffff to correspond to 1
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// exactly, so we need to drop some (8 for f32, 11
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// for f64) to guarantee the open end.
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(self.next_u32() >> IGNORED_BITS) as f32 / SCALE
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}
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/// Return the next random f64 selected from the half-open
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/// interval `[0, 1)`.
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///
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/// By default this is implemented in terms of `next_u64`, but a
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/// random number generator which can generate numbers satisfying
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/// the requirements directly can overload this for performance.
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/// It is required that the return value lies in `[0, 1)`.
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///
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/// See `Closed01` for the closed interval `[0,1]`, and
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/// `Open01` for the open interval `(0,1)`.
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fn next_f64(&mut self) -> f64 {
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const MANTISSA_BITS: usize = 53;
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const IGNORED_BITS: usize = 11;
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const SCALE: f64 = (1u64 << MANTISSA_BITS) as f64;
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(self.next_u64() >> IGNORED_BITS) as f64 / SCALE
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}
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/// Fill `dest` with random data.
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///
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/// This has a default implementation in terms of `next_u64` and
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/// `next_u32`, but should be overridden by implementations that
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/// offer a more efficient solution than just calling those
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/// methods repeatedly.
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///
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/// This method does *not* have a requirement to bear any fixed
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/// relationship to the other methods, for example, it does *not*
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/// have to result in the same output as progressively filling
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/// `dest` with `self.gen::<u8>()`, and any such behaviour should
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/// not be relied upon.
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///
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/// This method should guarantee that `dest` is entirely filled
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/// with new data, and may panic if this is impossible
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/// (e.g. reading past the end of a file that is being used as the
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/// source of randomness).
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fn fill_bytes(&mut self, dest: &mut [u8]) {
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// this could, in theory, be done by transmuting dest to a
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// [u64], but this is (1) likely to be undefined behaviour for
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// LLVM, (2) has to be very careful about alignment concerns,
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// (3) adds more `unsafe` that needs to be checked, (4)
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// probably doesn't give much performance gain if
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// optimisations are on.
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let mut count = 0;
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let mut num = 0;
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for byte in dest {
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if count == 0 {
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// we could micro-optimise here by generating a u32 if
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// we only need a few more bytes to fill the vector
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// (i.e. at most 4).
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num = self.next_u64();
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count = 8;
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}
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*byte = (num & 0xff) as u8;
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num >>= 8;
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count -= 1;
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}
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}
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/// Return a random value of a `Rand` type.
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#[inline(always)]
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fn gen<T: Rand>(&mut self) -> T {
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Rand::rand(self)
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}
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/// Return an iterator that will yield an infinite number of randomly
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/// generated items.
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fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> {
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Generator {
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rng: self,
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_marker: PhantomData,
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}
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}
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/// Generate a random value in the range [`low`, `high`).
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///
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/// This is a convenience wrapper around
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/// `distributions::Range`. If this function will be called
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/// repeatedly with the same arguments, one should use `Range`, as
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/// that will amortize the computations that allow for perfect
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/// uniformity, as they only happen on initialization.
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///
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/// # Panics
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///
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/// Panics if `low >= high`.
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fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T {
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assert!(low < high, "Rng.gen_range called with low >= high");
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Range::new(low, high).ind_sample(self)
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}
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/// Return a bool with a 1 in n chance of true
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fn gen_weighted_bool(&mut self, n: usize) -> bool {
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n <= 1 || self.gen_range(0, n) == 0
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}
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/// Return an iterator of random characters from the set A-Z,a-z,0-9.
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fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> {
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AsciiGenerator { rng: self }
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}
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/// Return a random element from `values`.
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///
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/// Return `None` if `values` is empty.
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fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> {
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if values.is_empty() {
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None
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} else {
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Some(&values[self.gen_range(0, values.len())])
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}
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}
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/// Shuffle a mutable slice in place.
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fn shuffle<T>(&mut self, values: &mut [T]) {
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let mut i = values.len();
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while i >= 2 {
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// invariant: elements with index >= i have been locked in place.
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i -= 1;
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// lock element i in place.
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values.swap(i, self.gen_range(0, i + 1));
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}
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}
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}
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/// Iterator which will generate a stream of random items.
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///
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/// This iterator is created via the `gen_iter` method on `Rng`.
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pub struct Generator<'a, T, R: 'a> {
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rng: &'a mut R,
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_marker: PhantomData<T>,
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}
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impl<'a, T: Rand, R: Rng> Iterator for Generator<'a, T, R> {
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type Item = T;
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fn next(&mut self) -> Option<T> {
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Some(self.rng.gen())
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}
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}
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/// Iterator which will continuously generate random ascii characters.
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///
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/// This iterator is created via the `gen_ascii_chars` method on `Rng`.
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pub struct AsciiGenerator<'a, R: 'a> {
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rng: &'a mut R,
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}
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impl<'a, R: Rng> Iterator for AsciiGenerator<'a, R> {
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type Item = char;
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fn next(&mut self) -> Option<char> {
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const GEN_ASCII_STR_CHARSET: &'static [u8] = b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
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abcdefghijklmnopqrstuvwxyz\
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0123456789";
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Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
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}
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}
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/// A random number generator that can be explicitly seeded to produce
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/// the same stream of randomness multiple times.
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pub trait SeedableRng<Seed>: Rng {
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/// Reseed an RNG with the given seed.
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fn reseed(&mut self, Seed);
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/// Create a new RNG with the given seed.
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fn from_seed(seed: Seed) -> Self;
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}
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/// An Xorshift[1] random number
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/// generator.
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///
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/// The Xorshift algorithm is not suitable for cryptographic purposes
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/// but is very fast. If you do not know for sure that it fits your
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/// requirements, use a more secure one such as `IsaacRng` or `OsRng`.
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///
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/// [1]: Marsaglia, George (July 2003). ["Xorshift
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/// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of
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/// Statistical Software*. Vol. 8 (Issue 14).
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#[derive(Clone)]
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pub struct XorShiftRng {
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x: u32,
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y: u32,
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z: u32,
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w: u32,
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}
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impl XorShiftRng {
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/// Creates a new XorShiftRng instance which is not seeded.
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///
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/// The initial values of this RNG are constants, so all generators created
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/// by this function will yield the same stream of random numbers. It is
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/// highly recommended that this is created through `SeedableRng` instead of
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/// this function
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pub fn new_unseeded() -> XorShiftRng {
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XorShiftRng {
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x: 0x193a6754,
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y: 0xa8a7d469,
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z: 0x97830e05,
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w: 0x113ba7bb,
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}
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}
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}
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impl Rng for XorShiftRng {
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#[inline]
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fn next_u32(&mut self) -> u32 {
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let x = self.x;
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let t = x ^ (x << 11);
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self.x = self.y;
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self.y = self.z;
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self.z = self.w;
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let w = self.w;
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self.w = w ^ (w >> 19) ^ (t ^ (t >> 8));
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self.w
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}
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}
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impl SeedableRng<[u32; 4]> for XorShiftRng {
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/// Reseed an XorShiftRng. This will panic if `seed` is entirely 0.
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fn reseed(&mut self, seed: [u32; 4]) {
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assert!(!seed.iter().all(|&x| x == 0),
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"XorShiftRng.reseed called with an all zero seed.");
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self.x = seed[0];
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self.y = seed[1];
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self.z = seed[2];
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self.w = seed[3];
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}
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/// Create a new XorShiftRng. This will panic if `seed` is entirely 0.
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fn from_seed(seed: [u32; 4]) -> XorShiftRng {
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assert!(!seed.iter().all(|&x| x == 0),
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"XorShiftRng::from_seed called with an all zero seed.");
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XorShiftRng {
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x: seed[0],
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y: seed[1],
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z: seed[2],
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w: seed[3],
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}
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}
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}
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impl Rand for XorShiftRng {
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fn rand<R: Rng>(rng: &mut R) -> XorShiftRng {
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let mut tuple: (u32, u32, u32, u32) = rng.gen();
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while tuple == (0, 0, 0, 0) {
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tuple = rng.gen();
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}
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let (x, y, z, w) = tuple;
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XorShiftRng {
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x: x,
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y: y,
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z: z,
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w: w,
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}
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}
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}
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/// A wrapper for generating floating point numbers uniformly in the
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/// open interval `(0,1)` (not including either endpoint).
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///
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/// Use `Closed01` for the closed interval `[0,1]`, and the default
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/// `Rand` implementation for `f32` and `f64` for the half-open
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/// `[0,1)`.
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pub struct Open01<F>(pub F);
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/// A wrapper for generating floating point numbers uniformly in the
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/// closed interval `[0,1]` (including both endpoints).
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///
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/// Use `Open01` for the closed interval `(0,1)`, and the default
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/// `Rand` implementation of `f32` and `f64` for the half-open
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/// `[0,1)`.
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pub struct Closed01<F>(pub F);
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#[cfg(test)]
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mod test {
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use std::__rand as rand;
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pub struct MyRng<R> {
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inner: R,
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}
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impl<R: rand::Rng> ::Rng for MyRng<R> {
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fn next_u32(&mut self) -> u32 {
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rand::Rng::next_u32(&mut self.inner)
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}
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}
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pub fn rng() -> MyRng<rand::ThreadRng> {
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MyRng { inner: rand::thread_rng() }
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}
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pub fn weak_rng() -> MyRng<rand::ThreadRng> {
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MyRng { inner: rand::thread_rng() }
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}
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}
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