Utilities for random number generation

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The key functions are random() and Rng::gen(). These are polymorphicand so can be used to generate any type that implements Rand. Type inferencemeans that often a simple call to rand::random() or rng.gen() willsuffice, but sometimes an annotation is required, e.g. rand::random::<f64>().

See the distributions submodule for sampling random numbers fromdistributions like normal and exponential.

There is built-in support for a RNG associated with each thread storedin thread-local storage. This RNG can be accessed via thread_rng, orused implicitly via random. This RNG is normally randomly seededfrom an operating-system source of randomness, e.g. /dev/urandom onUnix systems, and will automatically reseed itself from this sourceafter generating 32 KiB of random data.

TRNG v.4.13 Tina's Random Number Generator Library (TRNG) is a state of the art C pseudo-random number generator library for sequential and parallel Monte Carlo. JDiceChecker - Mac OS X Installer v.5.0.0.1 DiceLock Security JDiceChecker.JAR Library - Mac OS X installer. Incorporate random number tests in your applications. ‎Watch over your home from your iPhone, iPad or Mac with Ring’s Wi-Fi connected Video Doorbells and Security Cameras. Ring connects to your Wi-Fi network and sends you instant alerts when people press your Doorbell or trigger the built-in motion sensors. When you answer the alert, you can see, hear.

An application that requires an entropy source for cryptographic purposesmust use OsRng, which reads randomness from the source that the operatingsystem provides (e.g. /dev/urandom on Unixes or CryptGenRandom() on Windows).The other random number generators provided by this module are not suitablefor such purposes.

Note: many Unix systems provide /dev/random as well as /dev/urandom.This module uses /dev/urandom for the following reasons:

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  • 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 pseudorandomnumber 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 randomnessif 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 entropypool 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 viaReaderRng.
  • On some systems (e.g. FreeBSD, OpenBSD and Mac OS X) there is no differencebetween the two sources. (Also note that, on some systems e.g. FreeBSD, both /dev/randomand /dev/urandom may block once if the CSPRNG has not seeded yet.)
fn main() { use std::rand; use std::rand::Rng; let mut rng = rand::thread_rng(); if rng.gen() { // random bool println!('int: {}, uint: {}', rng.gen::<int>(), rng.gen::<uint>()) }}fn main() { use std::rand; let tuple = rand::random::<(f64, char)>(); println!('{:?}', tuple)}

Monte Carlo estimation of π

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For this example, imagine we have a square with sides of length 2 and a unitcircle, both centered at the origin. Since the area of a unit circle is π,we have:

So if we sample many points randomly from the square, roughly π / 4 of themshould be inside the circle.

We can use the above fact to estimate the value of π: pick many points in thesquare at random, calculate the fraction that fall within the circle, andmultiply this fraction by 4.

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use std::rand;use std::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 ifyou 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 stepsit will indeed confirm that it is better to switch.

use std::rand;use std::rand::Rng;use std::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<uint>, 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: uint, choice: uint, rng: &mut R) -> uint { 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: uint, open: uint) -> uint { free_doors(&[choice, open])[0]}fn free_doors(blocked: &[uint]) -> Vec<uint> { (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);}

Modules

Sampling from random distributions.

Interfaces to the operating system provided random numbergenerators.

A wrapper around any Reader to treat it as an RNG.

A wrapper around another RNG that reseeds it after itgenerates a certain number of random bytes.

Structs

A random number generator that uses the ChaCha20 algorithm [1].

A wrapper for generating floating point numbers uniformly in theclosed interval [0,1] (including both endpoints).

A random number generator that uses ISAAC-64[1], the 64-bitvariant of the ISAAC algorithm.

A random number generator that uses the ISAAC algorithm[1].

A wrapper for generating floating point numbers uniformly in theopen interval (0,1) (not including either endpoint).

A random number generator that retrieves randomness straight fromthe operating system. Balloon saga mac os. Platform sources:

Juegos de casino gratis tragamonedas para descargar. The standard RNG. This is designed to be efficient on the currentplatform.

The thread-local RNG.

An Xorshift[1] random numbergenerator.

Traits

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A type that can be randomly generated using an Rng.

Clean my macx free. A random number generator.

A random number generator that can be explicitly seeded to producethe same stream of randomness multiple times.

Functions

Generates a random value using the thread-local random number generator.

Randomly sample up to amount elements from an iterator.

Retrieve the lazily-initialized thread-local random numbergenerator, seeded by the system. Intended to be used in methodchaining style, e.g. thread_rng().gen::<int>().

Create a weak random number generator with a default algorithm and seed.