# [−][src]Trait rand::Rng

```pub trait Rng: RngCore {
fn gen<T>(&mut self) -> T    where        Standard: Distribution<T>,
{ ... }
fn gen_range<T, R>(&mut self, range: R) -> T    where        T: SampleUniform,        R: SampleRange<T>,
{ ... }
fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T { ... }
fn sample_iter<T, D>(self, distr: D) -> DistIter<D, Self, 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        D: Distribution<T>,        Self: Sized,
{ ... }
fn fill<T: Fill + ?Sized>(&mut self, dest: &mut T) { ... }
fn try_fill<T: Fill + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> { ... }
fn gen_bool(&mut self, p: f64) -> bool { ... }
fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool { ... }
}```

An automatically-implemented extension trait on `RngCore` providing high-level generic methods for sampling values and other convenience methods.

This is the primary trait to use when generating random values.

# Generic usage

The basic pattern is `fn foo<R: Rng + ?Sized>(rng: &mut R)`. Some things are worth noting here:

• Since `Rng: RngCore` and every `RngCore` implements `Rng`, it makes no difference whether we use `R: Rng` or `R: RngCore`.
• The `+ ?Sized` un-bounding allows functions to be called directly on type-erased references; i.e. `foo(r)` where `r: &mut RngCore`. Without this it would be necessary to write `foo(&mut r)`.

An alternative pattern is possible: `fn foo<R: Rng>(rng: R)`. This has some trade-offs. It allows the argument to be consumed directly without a `&mut` (which is how `from_rng(thread_rng())` works); also it still works directly on references (including type-erased references). Unfortunately within the function `foo` it is not known whether `rng` is a reference type or not, hence many uses of `rng` require an extra reference, either explicitly (`distr.sample(&mut rng)`) or implicitly (`rng.gen()`); one may hope the optimiser can remove redundant references later.

Example:

```use rand::Rng;

fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 {
rng.gen()
}
```

## Provided methods

### `fn gen<T>(&mut self) -> T where    Standard: Distribution<T>, `[src]

Return a random value supporting the `Standard` distribution.

# Example

```use rand::{thread_rng, Rng};

let x: u32 = rng.gen();
println!("{}", x);
println!("{:?}", rng.gen::<(f64, bool)>());```

# Arrays and tuples

The `rng.gen()` method is able to generate arrays (up to 32 elements) and tuples (up to 12 elements), so long as all element types can be generated.

For arrays of integers, especially for those with small element types (< 64 bit), it will likely be faster to instead use `Rng::fill`.

```use rand::{thread_rng, Rng};

let tuple: (u8, i32, char) = rng.gen(); // arbitrary tuple support

let arr1: [f32; 32] = rng.gen();        // array construction
let mut arr2 = [0u8; 128];
rng.fill(&mut arr2);                    // array fill```

### `fn gen_range<T, R>(&mut self, range: R) -> T where    T: SampleUniform,    R: SampleRange<T>, `[src]

Generate a random value in the given range.

This function is optimised for the case that only a single sample is made from the given range. See also the `Uniform` distribution type which may be faster if sampling from the same range repeatedly.

Only `gen_range(low..high)` and `gen_range(low..=high)` are supported.

# Panics

Panics if the range is empty.

# Example

```use rand::{thread_rng, Rng};

// Exclusive range
let n: u32 = rng.gen_range(0..10);
println!("{}", n);
let m: f64 = rng.gen_range(-40.0..1.3e5);
println!("{}", m);

// Inclusive range
let n: u32 = rng.gen_range(0..=10);
println!("{}", n);```

### `fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T`[src]

Sample a new value, using the given distribution.

### Example

```use rand::{thread_rng, Rng};
use rand::distributions::Uniform;

let x = rng.sample(Uniform::new(10u32, 15));
// Type annotation requires two types, the type and distribution; the
// distribution can be inferred.
let y = rng.sample::<u16, _>(Uniform::new(10, 15));```

### `fn sample_iter<T, D>(self, distr: D) -> DistIter<D, Self, 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    D: Distribution<T>,    Self: Sized, `[src]

Create an iterator that generates values using the given distribution.

Note that this function takes its arguments by value. This works since `(&mut R): Rng where R: Rng` and `(&D): Distribution where D: Distribution`, however borrowing is not automatic hence `rng.sample_iter(...)` may need to be replaced with `(&mut rng).sample_iter(...)`.

# Example

```use rand::{thread_rng, Rng};
use rand::distributions::{Alphanumeric, Uniform, Standard};

// Vec of 16 x f32:
let v: Vec<f32> = (&mut rng).sample_iter(Standard).take(16).collect();

// String:
let s: String = (&mut rng).sample_iter(Alphanumeric)
.take(7)
.map(char::from)
.collect();

// Combined values
println!("{:?}", (&mut rng).sample_iter(Standard).take(5)
.collect::<Vec<(f64, bool)>>());

// Dice-rolling:
let die_range = Uniform::new_inclusive(1, 6);
let mut roll_die = (&mut rng).sample_iter(die_range);
while roll_die.next().unwrap() != 6 {
println!("Not a 6; rolling again!");
}```

### `fn fill<T: Fill + ?Sized>(&mut self, dest: &mut T)`[src]

Fill any type implementing `Fill` with random data

The distribution is expected to be uniform with portable results, but this cannot be guaranteed for third-party implementations.

This is identical to `try_fill` except that it panics on error.

# Example

```use rand::{thread_rng, Rng};

let mut arr = [0i8; 20];

### `fn try_fill<T: Fill + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error>`[src]

Fill any type implementing `Fill` with random data

The distribution is expected to be uniform with portable results, but this cannot be guaranteed for third-party implementations.

This is identical to `fill` except that it forwards errors.

# Example

```use rand::{thread_rng, Rng};

let mut arr = [0u64; 4];
```

### `fn gen_bool(&mut self, p: f64) -> bool`[src]

Return a bool with a probability `p` of being true.

See also the `Bernoulli` distribution, which may be faster if sampling from the same probability repeatedly.

# Example

```use rand::{thread_rng, Rng};

println!("{}", rng.gen_bool(1.0 / 3.0));```

# Panics

If `p < 0` or `p > 1`.

### `fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool`[src]

Return a bool with a probability of `numerator/denominator` of being true. I.e. `gen_ratio(2, 3)` has chance of 2 in 3, or about 67%, of returning true. If `numerator == denominator`, then the returned value is guaranteed to be `true`. If `numerator == 0`, then the returned value is guaranteed to be `false`.

See also the `Bernoulli` distribution, which may be faster if sampling from the same `numerator` and `denominator` repeatedly.

# Panics

If `denominator == 0` or `numerator > denominator`.

# Example

```use rand::{thread_rng, Rng};