Struct ChiSquared

pub struct ChiSquared<F>
where
    F: Float,
    StandardNormal: Distribution<F>,
    Exp1: Distribution<F>,
    Open01: Distribution<F>,
{ /* private fields */ }

The chi-squared distribution χ²(k).

The chi-squared distribution is a continuous probability distribution with parameter k > 0 degrees of freedom.

For k > 0 integral, this distribution is the sum of the squares of k independent standard normal random variables. For other k, this uses the equivalent characterisation χ²(k) = Gamma(k/2, 2).

Plot

The plot shows the chi-squared distribution with various degrees of freedom.

Example

use rand_distr::{ChiSquared, Distribution};

let chi = ChiSquared::new(11.0).unwrap();
let v = chi.sample(&mut rand::rng());
println!("{} is from a χ²(11) distribution", v)

Implementations

impl<F> ChiSquared<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

pub fn new(k: F) -> Result<ChiSquared<F>, Error>

Create a new chi-squared distribution with degrees-of-freedom k.

Trait Implementations

impl<F> Clone for ChiSquared<F>

where F: Clone + Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn clone(&self) -> ChiSquared<F>

Returns a duplicate of the value.

const fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<F> Debug for ChiSquared<F>

where F: Debug + Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<'de, F> Deserialize<'de> for ChiSquared<F>

where F: Float + Deserialize<'de>, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn deserialize<__D>( __deserializer: __D, ) -> Result<ChiSquared<F>, <__D as Deserializer<'de>>::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F> Distribution<F> for ChiSquared<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn sample<R>(&self, rng: &mut R) -> F

where R: Rng + ?Sized,

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

fn sample_iter<R>(self, rng: R) -> Iter<Self, R, T>

where R: Rng, Self: Sized,

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

fn map<F, S>(self, func: F) -> Map<Self, F, T, S>

where F: Fn(T) -> S, Self: Sized,

Map sampled values to type S

impl<F> PartialEq for ChiSquared<F>

where F: PartialEq + Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn eq(&self, other: &ChiSquared<F>) -> bool

Tests for self and other values to be equal, and is used by ==.

const fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<F> Serialize for ChiSquared<F>

where F: Float + Serialize, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn serialize<__S>( &self, __serializer: __S, ) -> Result<<__S as Serializer>::Ok, <__S as Serializer>::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<F> Copy for ChiSquared<F>

where F: Copy + Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

impl<F> StructuralPartialEq for ChiSquared<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,