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Move user-guide documentation to the book
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‎src/distributions/exponential.rs‎

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@@ -63,6 +63,8 @@ impl Distribution<f64> for Exp1 {
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///
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/// This distribution has density function: `f(x) = lambda * exp(-lambda * x)`
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/// for `x > 0`.
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///
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/// Note that [`Exp1`](struct.Exp1.html) is an optimised implementation for `lambda = 1`.
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///
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/// # Example
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///

‎src/distributions/normal.rs‎

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@@ -74,8 +74,11 @@ impl Distribution<f64> for StandardNormal {
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/// The normal distribution `N(mean, std_dev**2)`.
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///
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/// This uses the ZIGNOR variant of the Ziggurat method, see `StandardNormal`
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/// This uses the ZIGNOR variant of the Ziggurat method, see [`StandardNormal`]
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/// for more details.
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///
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/// Note that [`StandardNormal`] is an optimised implementation for mean 0, and
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/// standard deviation 1.
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///
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/// # Example
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///
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/// let v = normal.sample(&mut rand::thread_rng());
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/// println!("{} is from a N(2, 9) distribution", v)
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/// ```
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///
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/// [`StandardNormal`]: struct.StandardNormal.html
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#[derive(Clone, Copy, Debug)]
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pub struct Normal {
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mean: f64,

‎src/lib.rs‎

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//! Rand provides utilities to generate random numbers, to convert them to
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//! useful types and distributions, and some randomness-related algorithms.
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//!
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//! # Basic usage
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//!
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//! # Quick Start
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//!
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//! To get you started quickly, the easiest and highest-level way to get
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//! a random value is to use [`random()`].
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//! a random value is to use [`random()`]; alternatively you can use
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//! [`thread_rng()`]. The [`Rng`] trait provides a useful API on all RNGs, while
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//! the [`distributions` module] and [`seq` module] provide further
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//! functionality on top of RNGs.
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//!
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//! ```
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//! let x: u8 = rand::random(); // generates an integer within u8 bounds
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//! println!("{}", x);
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//!
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//! let y = rand::random::<f64>(); // generates a float between 0 and 1
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//! println!("{}", y);
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//!
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//! use rand::prelude::*;
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//!
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//! if rand::random() { // generates a boolean
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//! println!("Heads!");
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//! // Try printing a random unicode code point (probably a bad idea)!
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//! println!("char: {}", rand::random::<char>());
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//! }
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//! ```
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//!
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//! This supports generating most common types but is not very flexible, thus
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//! you probably want to learn a bit more about the Rand library.
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//!
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//!
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//! # The two-step process to get a random value
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//!
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//! Generating random values is typically a two-step process:
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//!
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//! - get some *random data* (an integer or bit/byte sequence) from a random
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//! number generator (RNG);
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//! - use some function to transform that *data* into the type of value you want
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//! (this function is an implementation of some *distribution* describing the
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//! kind of value produced).
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//!
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//! Rand represents the first step with the [`RngCore`] trait and the second
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//! step via a combination of the [`Rng`] extension trait and the
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//! [`distributions` module].
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//! In practice you probably won't use [`RngCore`] directly unless you are
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//! implementing a random number generator (RNG).
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//!
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//! There are many kinds of RNGs, with different trade-offs. You can read more
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//! about them in the [`rngs` module] and even more in the [`prng` module],
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//! however, often you can just use [`thread_rng()`]. This function
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//! automatically initializes an RNG in thread-local memory, then returns a
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//! reference to it. It is fast, good quality, and secure (unpredictable).
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//!
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//! To turn the output of the RNG into something usable, you usually want to use
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//! the methods from the [`Rng`] trait. Some of the most useful methods are:
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//!
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//! - [`gen`] generates a random value appropriate for the type (just like
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//! [`random()`]). For integers this is normally the full representable range
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//! (e.g. from `0u32` to `std::u32::MAX`), for floats this is between 0 and 1,
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//! and some other types are supported, including arrays and tuples. See the
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//! [`Standard`] distribution which provides the implementations.
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//! - [`gen_range`] samples from a specific range of values; this is like
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//! [`gen`] but with specific upper and lower bounds.
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//! - [`sample`] samples directly from some distribution.
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//!
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//! [`random()`] is defined using just the above: `thread_rng().gen()`.
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//!
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//! ## Distributions
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//!
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//! What are distributions, you ask? Specifying only the type and range of
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//! values (known as the *sample space*) is not enough; samples must also have
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//! a *probability distribution*, describing the relative probability of
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//! sampling each value in that space.
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//!
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//! In many cases a *uniform* distribution is used, meaning roughly that each
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//! value is equally likely (or for "continuous" types like floats, that each
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//! equal-sized sub-range has the same probability of containing a sample).
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//! [`gen`] and [`gen_range`] both use statistically uniform distributions.
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//!
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//! The [`distributions` module] provides implementations
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//! of some other distributions, including Normal, Log-Normal and Exponential.
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//!
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//! It is worth noting that the functionality already mentioned is implemented
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//! with distributions: [`gen`] samples values using the [`Standard`]
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//! distribution, while [`gen_range`] uses [`Uniform`].
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//!
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//! ## Importing (prelude)
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//!
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//! The most convenient way to import items from Rand is to use the [prelude].
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//! This includes the most important parts of Rand, but only those unlikely to
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//! cause name conflicts.
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//!
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//! Note that Rand 0.5 has significantly changed the module organization and
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//! contents relative to previous versions. Where possible old names have been
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//! kept (but are hidden in the documentation), however these will be removed
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//! in the future. We therefore recommend migrating to use the prelude or the
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//! new module organization in your imports.
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//!
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//!
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//! ## Examples
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//! let mut rng = rand::thread_rng();
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//! let y: f64 = rng.gen(); // generates a float between 0 and 1
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//!
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//! let nums: Vec<i32> = (1..100).collect();
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//! nums.shuffle(&mut rng);
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//! ```
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//! use rand::prelude::*;
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//!
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//! // thread_rng is often the most convenient source of randomness:
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//! let mut rng = thread_rng();
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//!
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//! if rng.gen() { // random bool
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//! let x: f64 = rng.gen(); // random number in range [0, 1)
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//! println!("x is: {}", x);
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//! let ch = rng.gen::<char>(); // using type annotation
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//! println!("char is: {}", ch);
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//! println!("Number from 0 to 9: {}", rng.gen_range(0, 10));
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//! }
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//! ```
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//!
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//!
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//! # More functionality
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//!
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//! The [`Rng`] trait includes a few more methods not mentioned above:
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//!
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//! - [`Rng::sample_iter`] allows iterating over values from a chosen
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//! distribution.
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//! - [`Rng::gen_bool`] generates boolean "events" with a given probability.
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//! - [`Rng::fill`] and [`Rng::try_fill`] are fast alternatives to fill a slice
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//! of integers.
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//! - [`Rng::shuffle`] randomly shuffles elements in a slice.
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//! - [`Rng::choose`] picks one element at random from a slice.
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//!
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//! For more slice/sequence related functionality, look in the [`seq` module].
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//!
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//!
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//! # Error handling
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//!
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//! Error handling in Rand is a compromise between simplicity and necessity.
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//! Most RNGs and sampling functions will never produce errors, and making these
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//! able to handle errors would add significant overhead (to code complexity
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//! and ergonomics of usage at least, and potentially also performance,
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//! depending on the approach).
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//! However, external RNGs can fail, and being able to handle this is important.
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//!
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//! It has therefore been decided that *most* methods should not return a
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//! `Result` type, with as exceptions [`Rng::try_fill`],
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//! [`RngCore::try_fill_bytes`], and [`SeedableRng::from_rng`].
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//!
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//! Note that it is the RNG that panics when it fails but is not used through a
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//! method that can report errors. Currently Rand contains only three RNGs that
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//! can return an error (and thus may panic), and documents this property:
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//! [`OsRng`], [`EntropyRng`] and [`ReadRng`]. Other RNGs, like [`ThreadRng`]
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//! and [`StdRng`], can be used with all methods without concern.
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//!
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//! One further problem is that if Rand is unable to get any external randomness
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//! when initializing an RNG with [`EntropyRng`], it will panic in
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//! [`FromEntropy::from_entropy`], and notably in [`thread_rng()`]. Except by
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//! compromising security, this problem is as unsolvable as running out of
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//! memory.
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//!
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//!
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//! # Distinction between Rand and `rand_core`
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//!
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//! The [`rand_core`] crate provides the necessary traits and functionality for
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//! implementing RNGs; this includes the [`RngCore`] and [`SeedableRng`] traits
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//! and the [`Error`] type.
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//! Crates implementing RNGs should depend on [`rand_core`].
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//!
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//! Applications and libraries consuming random values are encouraged to use the
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//! Rand crate, which re-exports the common parts of [`rand_core`].
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//!
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//!
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//! # More examples
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//!
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//! For some inspiration, see the examples:
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//!
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//! - [Monte Carlo estimation of π](
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//! https://github.com/rust-random/rand/blob/master/examples/monte-carlo.rs)
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//! - [Monty Hall Problem](
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//! https://github.com/rust-random/rand/blob/master/examples/monty-hall.rs)
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//!
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//! # The Book
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//!
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//! For the user guide and futher documentation, please read
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//! [The Rust Rand Book](https://rust-random.github.io/book).
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//!
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//! [`distributions` module]: distributions/index.html
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//! [`FromEntropy::from_entropy`]: trait.FromEntropy.html#tymethod.from_entropy
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//! [`EntropyRng`]: rngs/struct.EntropyRng.html
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//! [`Error`]: struct.Error.html
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//! [`gen_range`]: trait.Rng.html#method.gen_range
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//! [`gen`]: trait.Rng.html#method.gen
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//! [`OsRng`]: rngs/struct.OsRng.html
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//! [prelude]: prelude/index.html
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//! [`rand_core`]: https://crates.io/crates/rand_core
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//! [`random()`]: fn.random.html
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//! [`ReadRng`]: rngs/adapter/struct.ReadRng.html
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//! [`Rng::choose`]: trait.Rng.html#method.choose
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//! [`Rng::fill`]: trait.Rng.html#method.fill
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//! [`Rng::gen_bool`]: trait.Rng.html#method.gen_bool
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//! [`Rng::gen`]: trait.Rng.html#method.gen
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//! [`Rng::sample_iter`]: trait.Rng.html#method.sample_iter
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//! [`Rng::shuffle`]: trait.Rng.html#method.shuffle
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//! [`RngCore`]: trait.RngCore.html
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//! [`RngCore::try_fill_bytes`]: trait.RngCore.html#method.try_fill_bytes
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//! [`rngs` module]: rngs/index.html
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//! [`prng` module]: prng/index.html
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//! [`Rng`]: trait.Rng.html
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//! [`Rng::try_fill`]: trait.Rng.html#method.try_fill
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//! [`sample`]: trait.Rng.html#method.sample
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//! [`SeedableRng`]: trait.SeedableRng.html
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//! [`SeedableRng::from_rng`]: trait.SeedableRng.html#method.from_rng
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//! [`seq` module]: seq/index.html
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//! [`SmallRng`]: rngs/struct.SmallRng.html
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//! [`StdRng`]: rngs/struct.StdRng.html
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//! [`thread_rng()`]: fn.thread_rng.html
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//! [`ThreadRng`]: rngs/struct.ThreadRng.html
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//! [`Standard`]: distributions/struct.Standard.html
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//! [`Uniform`]: distributions/struct.Uniform.html
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#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",

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