From b5aa889f7fced8ba2cc1698ae9696d7bd0ca8ab5 Mon Sep 17 00:00:00 2001 From: garhve Date: Tue, 20 Dec 2022 11:07:35 +0800 Subject: remove compiled binary --- .../doc/rand/distributions/trait.Distribution.html | 65 ---------------------- 1 file changed, 65 deletions(-) delete mode 100644 rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/rand/distributions/trait.Distribution.html (limited to 'rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/rand/distributions/trait.Distribution.html') diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/rand/distributions/trait.Distribution.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/rand/distributions/trait.Distribution.html deleted file mode 100644 index 32c5f9d..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/rand/distributions/trait.Distribution.html +++ /dev/null @@ -1,65 +0,0 @@ -Distribution in rand::distributions - Rust

Trait rand::distributions::Distribution

source · [−]
pub trait Distribution<T> {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T;
-
-    fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>ⓘNotable traits for DistIter<D, R, T>impl<D, R, T> Iterator for DistIter<D, R, T>where
    D: Distribution<T>,
    R: Rng,    type Item = T;
    where
        R: Rng,
        Self: Sized,
-    { ... }
-    fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>
    where
        F: Fn(T) -> S,
        Self: Sized,
-    { ... }
-}
Expand description

Types (distributions) that can be used to create a random instance of T.

-

It is possible to sample from a distribution through both the -Distribution and Rng traits, via distr.sample(&mut rng) and -rng.sample(distr). They also both offer the sample_iter method, which -produces an iterator that samples from the distribution.

-

All implementations are expected to be immutable; this has the significant -advantage of not needing to consider thread safety, and for most -distributions efficient state-less sampling algorithms are available.

-

Implementations are typically expected to be portable with reproducible -results when used with a PRNG with fixed seed; see the -portability chapter -of The Rust Rand Book. In some cases this does not apply, e.g. the usize -type requires different sampling on 32-bit and 64-bit machines.

-

Required Methods

Generate a random value of T, using rng as the source of randomness.

-

Provided Methods

Create an iterator that generates random values of T, using rng as -the source of randomness.

-

Note that this function takes self by value. This works since -Distribution<T> is impl’d for &D where D: Distribution<T>, -however borrowing is not automatic hence distr.sample_iter(...) may -need to be replaced with (&distr).sample_iter(...) to borrow or -(&*distr).sample_iter(...) to reborrow an existing reference.

-
Example
-
use rand::thread_rng;
-use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard};
-
-let mut rng = thread_rng();
-
-// Vec of 16 x f32:
-let v: Vec<f32> = Standard.sample_iter(&mut rng).take(16).collect();
-
-// String:
-let s: String = Alphanumeric
-    .sample_iter(&mut rng)
-    .take(7)
-    .map(char::from)
-    .collect();
-
-// Dice-rolling:
-let die_range = Uniform::new_inclusive(1, 6);
-let mut roll_die = die_range.sample_iter(&mut rng);
-while roll_die.next().unwrap() != 6 {
-    println!("Not a 6; rolling again!");
-}
-

Create a distribution of values of ‘S’ by mapping the output of Self -through the closure F

-
Example
-
use rand::thread_rng;
-use rand::distributions::{Distribution, Uniform};
-
-let mut rng = thread_rng();
-
-let die = Uniform::new_inclusive(1, 6);
-let even_number = die.map(|num| num % 2 == 0);
-while !even_number.sample(&mut rng) {
-    println!("Still odd; rolling again!");
-}
-

Implementations on Foreign Types

Implementors

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