Struct Normal 

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

The normal distribution N(mean, std_dev**2).

This uses the ZIGNOR variant of the Ziggurat method, see StandardNormal for more details.

Note that StandardNormal is an optimised implementation for mean 0, and standard deviation 1.

Example

use rand_distr::{Normal, Distribution};

// mean 2, standard deviation 3
let normal = Normal::new(2.0, 3.0).unwrap();
let v = normal.sample(&mut rand::thread_rng());
println!("{} is from a N(2, 9) distribution", v)

Implementations

impl<F> Normal<F>

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

pub fn new(mean: F, std_dev: F) -> Result<Normal<F>, Error>

Construct, from mean and standard deviation

Parameters:

• mean (μ, unrestricted)
• standard deviation (σ, must be finite)

pub fn from_mean_cv(mean: F, cv: F) -> Result<Normal<F>, Error>

Construct, from mean and coefficient of variation

Parameters:

• mean (μ, unrestricted)
• coefficient of variation (cv = abs(σ / μ))

pub fn from_zscore(&self, zscore: F) -> F

Sample from a z-score

This may be useful for generating correlated samples x1 and x2 from two different distributions, as follows.

let mut rng = thread_rng();
let z = StandardNormal.sample(&mut rng);
let x1 = Normal::new(0.0, 1.0).unwrap().from_zscore(z);
let x2 = Normal::new(2.0, -3.0).unwrap().from_zscore(z);

pub fn mean(&self) -> F

Returns the mean (μ) of the distribution.

pub fn std_dev(&self) -> F

Returns the standard deviation (σ) of the distribution.

Trait Implementations

impl<F> Clone for Normal<F>

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

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F> Debug for Normal<F>

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

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

Formats the value using the given formatter.

impl<F> Distribution<F> for Normal<F>

where F: Float, StandardNormal: 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) -> DistIter<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) -> DistMap<Self, F, T, S>

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

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

impl<F> Copy for Normal<F>

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